In [11]:
# display random image
plt.imshow(X_train[30]);
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Computer Vision Project Solution
• DOMAIN: Entertainment
• CONTEXT: Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene.The data labelling is already done. Since there higher time complexity is involved in the
• DATA DESCRIPTION: The dataset comprises of images and its mask for corresponding human face.
• PROJECT OBJECTIVE: To build a face detection system.
# Import all the necessary libraries.
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.metrics import classification_report
from sklearn import model_selection
from sklearn.compose import ColumnTransformer
import matplotlib.pyplot as plt
import cv2
from PIL import Image
import seaborn as sns
from sklearn.impute import SimpleImputer
import warnings
from sklearn.metrics import confusion_matrix
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
import tensorflow as tf
import random
random.seed(1)
np.random.seed(1)
tf.random.set_seed(1)
warnings.filterwarnings("ignore")
from keras import losses
from keras import optimizers
from keras import Sequential
from keras.layers import Dense
from keras import backend
from keras.layers import BatchNormalization
from keras.layers import Dropout
from keras.applications.mobilenet import preprocess_input
from keras.applications import MobileNetV2
from keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape,Input,BatchNormalization,Activation,MaxPooling2D,Dropout,Conv2DTranspose,concatenate
from keras.models import Model
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
1A.Import and read ‘images.npy’.
data1 = np.load('/content/drive/MyDrive/Colab Notebooks/images.npy', allow_pickle=True)
data1.shape
(409, 2)
data1[0]
array([array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8),
list([{'label': ['Face'], 'notes': '', 'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063}, {'x': 0.1723076923076923, 'y': 0.45345345345345345}], 'imageWidth': 650, 'imageHeight': 333}, {'label': ['Face'], 'notes': '', 'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913}, {'x': 0.6584615384615384, 'y': 0.46846846846846846}], 'imageWidth': 650, 'imageHeight': 333}])],
dtype=object)
data1[0][0]
array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8)
data1[0][1]
[{'label': ['Face'],
'notes': '',
'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063},
{'x': 0.1723076923076923, 'y': 0.45345345345345345}],
'imageWidth': 650,
'imageHeight': 333},
{'label': ['Face'],
'notes': '',
'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913},
{'x': 0.6584615384615384, 'y': 0.46846846846846846}],
'imageWidth': 650,
'imageHeight': 333}]
1B.Split the data into Features(X) & labels(Y). Unify shape of all the images.
# Set image height and width to unify shape of all the images to same size.
IMAGE_WIDTH = 224
IMAGE_HEIGHT = 224
# Feature(X) : image
X = np.zeros((int(data1.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH, 3)) # RGB hence 3 channels
# Label(Y) : mask
Y = np.zeros((int(data1.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH))
# Read the parameter available from the file for each image and extract images and mask coordinates details
for index in range(data1.shape[0]):
image = cv2.resize(data1[index][0], dsize = (IMAGE_HEIGHT, IMAGE_WIDTH), interpolation = cv2.INTER_CUBIC)
try:
image = image[:, :, :3]
except:
continue
X[index] = preprocess_input(np.array(image, dtype = np.float32))
for i in data1[index][1]:
x1 = int(i["points"][0]['x'] * IMAGE_WIDTH)
x2 = int(i["points"][1]['x'] * IMAGE_WIDTH)
y1 = int(i["points"][0]['y'] * IMAGE_HEIGHT)
y2 = int(i["points"][1]['y'] * IMAGE_HEIGHT)
# Replace all the pixels within masked area with 1.
Y[index][y1:y2, x1:x2] = 1
1C.Split the data into train and test[400:9].
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.022, random_state = 0, shuffle = False)
print("X_Train Shape: ", X_train.shape)
print("y_Train Shape: ", y_train.shape)
print("X_Test Shape: ", X_test.shape)
print("y_Test Shape: ", y_test.shape)
X_Train Shape: (400, 224, 224, 3) y_Train Shape: (400, 224, 224) X_Test Shape: (9, 224, 224, 3) y_Test Shape: (9, 224, 224)
1D.Select random image from the train data and display original image and masked image.
# display random image
plt.imshow(X_train[30]);
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
plt.imshow(y_train[30]);
plt.imshow(X_test[0]);
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
plt.imshow(y_test[0]);
2A.Design a face mask detection model.
Using MobileNet architecture for initial pre-trained non-trainable layers.
baseModel = MobileNetV2(weights="imagenet", include_top=False, input_tensor=Input(shape=(224, 224, 3)))
baseModel.summary()
WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5
9406464/9406464 [==============================] - 0s 0us/step
Model: "mobilenetv2_1.00_224"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3 0 []
)]
Conv1 (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]']
)
bn_Conv1 (BatchNormalization) (None, 112, 112, 32 128 ['Conv1[0][0]']
)
Conv1_relu (ReLU) (None, 112, 112, 32 0 ['bn_Conv1[0][0]']
)
expanded_conv_depthwise (Depth (None, 112, 112, 32 288 ['Conv1_relu[0][0]']
wiseConv2D) )
expanded_conv_depthwise_BN (Ba (None, 112, 112, 32 128 ['expanded_conv_depthwise[0][0]']
tchNormalization) )
expanded_conv_depthwise_relu ( (None, 112, 112, 32 0 ['expanded_conv_depthwise_BN[0][0
ReLU) ) ]']
expanded_conv_project (Conv2D) (None, 112, 112, 16 512 ['expanded_conv_depthwise_relu[0]
) [0]']
expanded_conv_project_BN (Batc (None, 112, 112, 16 64 ['expanded_conv_project[0][0]']
hNormalization) )
block_1_expand (Conv2D) (None, 112, 112, 96 1536 ['expanded_conv_project_BN[0][0]'
) ]
block_1_expand_BN (BatchNormal (None, 112, 112, 96 384 ['block_1_expand[0][0]']
ization) )
block_1_expand_relu (ReLU) (None, 112, 112, 96 0 ['block_1_expand_BN[0][0]']
)
block_1_pad (ZeroPadding2D) (None, 113, 113, 96 0 ['block_1_expand_relu[0][0]']
)
block_1_depthwise (DepthwiseCo (None, 56, 56, 96) 864 ['block_1_pad[0][0]']
nv2D)
block_1_depthwise_BN (BatchNor (None, 56, 56, 96) 384 ['block_1_depthwise[0][0]']
malization)
block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 ['block_1_depthwise_BN[0][0]']
block_1_project (Conv2D) (None, 56, 56, 24) 2304 ['block_1_depthwise_relu[0][0]']
block_1_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_1_project[0][0]']
lization)
block_2_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_1_project_BN[0][0]']
block_2_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_2_expand[0][0]']
ization)
block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_expand_BN[0][0]']
block_2_depthwise (DepthwiseCo (None, 56, 56, 144) 1296 ['block_2_expand_relu[0][0]']
nv2D)
block_2_depthwise_BN (BatchNor (None, 56, 56, 144) 576 ['block_2_depthwise[0][0]']
malization)
block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_depthwise_BN[0][0]']
block_2_project (Conv2D) (None, 56, 56, 24) 3456 ['block_2_depthwise_relu[0][0]']
block_2_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_2_project[0][0]']
lization)
block_2_add (Add) (None, 56, 56, 24) 0 ['block_1_project_BN[0][0]',
'block_2_project_BN[0][0]']
block_3_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_2_add[0][0]']
block_3_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_3_expand[0][0]']
ization)
block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_3_expand_BN[0][0]']
block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 ['block_3_expand_relu[0][0]']
block_3_depthwise (DepthwiseCo (None, 28, 28, 144) 1296 ['block_3_pad[0][0]']
nv2D)
block_3_depthwise_BN (BatchNor (None, 28, 28, 144) 576 ['block_3_depthwise[0][0]']
malization)
block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 ['block_3_depthwise_BN[0][0]']
block_3_project (Conv2D) (None, 28, 28, 32) 4608 ['block_3_depthwise_relu[0][0]']
block_3_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_3_project[0][0]']
lization)
block_4_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_3_project_BN[0][0]']
block_4_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_4_expand[0][0]']
ization)
block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_expand_BN[0][0]']
block_4_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_4_expand_relu[0][0]']
nv2D)
block_4_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_4_depthwise[0][0]']
malization)
block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_depthwise_BN[0][0]']
block_4_project (Conv2D) (None, 28, 28, 32) 6144 ['block_4_depthwise_relu[0][0]']
block_4_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_4_project[0][0]']
lization)
block_4_add (Add) (None, 28, 28, 32) 0 ['block_3_project_BN[0][0]',
'block_4_project_BN[0][0]']
block_5_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_4_add[0][0]']
block_5_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_5_expand[0][0]']
ization)
block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_expand_BN[0][0]']
block_5_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_5_expand_relu[0][0]']
nv2D)
block_5_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_5_depthwise[0][0]']
malization)
block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_depthwise_BN[0][0]']
block_5_project (Conv2D) (None, 28, 28, 32) 6144 ['block_5_depthwise_relu[0][0]']
block_5_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_5_project[0][0]']
lization)
block_5_add (Add) (None, 28, 28, 32) 0 ['block_4_add[0][0]',
'block_5_project_BN[0][0]']
block_6_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_5_add[0][0]']
block_6_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_6_expand[0][0]']
ization)
block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_6_expand_BN[0][0]']
block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 ['block_6_expand_relu[0][0]']
block_6_depthwise (DepthwiseCo (None, 14, 14, 192) 1728 ['block_6_pad[0][0]']
nv2D)
block_6_depthwise_BN (BatchNor (None, 14, 14, 192) 768 ['block_6_depthwise[0][0]']
malization)
block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 ['block_6_depthwise_BN[0][0]']
block_6_project (Conv2D) (None, 14, 14, 64) 12288 ['block_6_depthwise_relu[0][0]']
block_6_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_6_project[0][0]']
lization)
block_7_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_6_project_BN[0][0]']
block_7_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_7_expand[0][0]']
ization)
block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_expand_BN[0][0]']
block_7_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_7_expand_relu[0][0]']
nv2D)
block_7_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_7_depthwise[0][0]']
malization)
block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_depthwise_BN[0][0]']
block_7_project (Conv2D) (None, 14, 14, 64) 24576 ['block_7_depthwise_relu[0][0]']
block_7_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_7_project[0][0]']
lization)
block_7_add (Add) (None, 14, 14, 64) 0 ['block_6_project_BN[0][0]',
'block_7_project_BN[0][0]']
block_8_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_7_add[0][0]']
block_8_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_8_expand[0][0]']
ization)
block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_expand_BN[0][0]']
block_8_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_8_expand_relu[0][0]']
nv2D)
block_8_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_8_depthwise[0][0]']
malization)
block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_depthwise_BN[0][0]']
block_8_project (Conv2D) (None, 14, 14, 64) 24576 ['block_8_depthwise_relu[0][0]']
block_8_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_8_project[0][0]']
lization)
block_8_add (Add) (None, 14, 14, 64) 0 ['block_7_add[0][0]',
'block_8_project_BN[0][0]']
block_9_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_8_add[0][0]']
block_9_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_9_expand[0][0]']
ization)
block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_expand_BN[0][0]']
block_9_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_9_expand_relu[0][0]']
nv2D)
block_9_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_9_depthwise[0][0]']
malization)
block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_depthwise_BN[0][0]']
block_9_project (Conv2D) (None, 14, 14, 64) 24576 ['block_9_depthwise_relu[0][0]']
block_9_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_9_project[0][0]']
lization)
block_9_add (Add) (None, 14, 14, 64) 0 ['block_8_add[0][0]',
'block_9_project_BN[0][0]']
block_10_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_9_add[0][0]']
block_10_expand_BN (BatchNorma (None, 14, 14, 384) 1536 ['block_10_expand[0][0]']
lization)
block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_expand_BN[0][0]']
block_10_depthwise (DepthwiseC (None, 14, 14, 384) 3456 ['block_10_expand_relu[0][0]']
onv2D)
block_10_depthwise_BN (BatchNo (None, 14, 14, 384) 1536 ['block_10_depthwise[0][0]']
rmalization)
block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_depthwise_BN[0][0]']
block_10_project (Conv2D) (None, 14, 14, 96) 36864 ['block_10_depthwise_relu[0][0]']
block_10_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_10_project[0][0]']
alization)
block_11_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_10_project_BN[0][0]']
block_11_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_11_expand[0][0]']
lization)
block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_expand_BN[0][0]']
block_11_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_11_expand_relu[0][0]']
onv2D)
block_11_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_11_depthwise[0][0]']
rmalization)
block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_depthwise_BN[0][0]']
block_11_project (Conv2D) (None, 14, 14, 96) 55296 ['block_11_depthwise_relu[0][0]']
block_11_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_11_project[0][0]']
alization)
block_11_add (Add) (None, 14, 14, 96) 0 ['block_10_project_BN[0][0]',
'block_11_project_BN[0][0]']
block_12_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_11_add[0][0]']
block_12_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_12_expand[0][0]']
lization)
block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_expand_BN[0][0]']
block_12_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_12_expand_relu[0][0]']
onv2D)
block_12_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_12_depthwise[0][0]']
rmalization)
block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_depthwise_BN[0][0]']
block_12_project (Conv2D) (None, 14, 14, 96) 55296 ['block_12_depthwise_relu[0][0]']
block_12_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_12_project[0][0]']
alization)
block_12_add (Add) (None, 14, 14, 96) 0 ['block_11_add[0][0]',
'block_12_project_BN[0][0]']
block_13_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_12_add[0][0]']
block_13_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_13_expand[0][0]']
lization)
block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_13_expand_BN[0][0]']
block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 ['block_13_expand_relu[0][0]']
block_13_depthwise (DepthwiseC (None, 7, 7, 576) 5184 ['block_13_pad[0][0]']
onv2D)
block_13_depthwise_BN (BatchNo (None, 7, 7, 576) 2304 ['block_13_depthwise[0][0]']
rmalization)
block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 ['block_13_depthwise_BN[0][0]']
block_13_project (Conv2D) (None, 7, 7, 160) 92160 ['block_13_depthwise_relu[0][0]']
block_13_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_13_project[0][0]']
alization)
block_14_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_13_project_BN[0][0]']
block_14_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_14_expand[0][0]']
lization)
block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_expand_BN[0][0]']
block_14_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_14_expand_relu[0][0]']
onv2D)
block_14_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_14_depthwise[0][0]']
rmalization)
block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_depthwise_BN[0][0]']
block_14_project (Conv2D) (None, 7, 7, 160) 153600 ['block_14_depthwise_relu[0][0]']
block_14_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_14_project[0][0]']
alization)
block_14_add (Add) (None, 7, 7, 160) 0 ['block_13_project_BN[0][0]',
'block_14_project_BN[0][0]']
block_15_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_14_add[0][0]']
block_15_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_15_expand[0][0]']
lization)
block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_expand_BN[0][0]']
block_15_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_15_expand_relu[0][0]']
onv2D)
block_15_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_15_depthwise[0][0]']
rmalization)
block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_depthwise_BN[0][0]']
block_15_project (Conv2D) (None, 7, 7, 160) 153600 ['block_15_depthwise_relu[0][0]']
block_15_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_15_project[0][0]']
alization)
block_15_add (Add) (None, 7, 7, 160) 0 ['block_14_add[0][0]',
'block_15_project_BN[0][0]']
block_16_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_15_add[0][0]']
block_16_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_16_expand[0][0]']
lization)
block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_expand_BN[0][0]']
block_16_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_16_expand_relu[0][0]']
onv2D)
block_16_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_16_depthwise[0][0]']
rmalization)
block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_depthwise_BN[0][0]']
block_16_project (Conv2D) (None, 7, 7, 320) 307200 ['block_16_depthwise_relu[0][0]']
block_16_project_BN (BatchNorm (None, 7, 7, 320) 1280 ['block_16_project[0][0]']
alization)
Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 ['block_16_project_BN[0][0]']
Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['Conv_1[0][0]']
out_relu (ReLU) (None, 7, 7, 1280) 0 ['Conv_1_bn[0][0]']
==================================================================================================
Total params: 2,257,984
Trainable params: 2,223,872
Non-trainable params: 34,112
__________________________________________________________________________________________________
Add appropriate Upsampling layers to imitate U-net architecture.
def create_conv_block(prevlayer, filters, prefix ):
conv = Conv2D(filters, (3, 3), padding = 'same', kernel_initializer = 'he_normal', strides = (1, 1), name = prefix + '_conv')(prevlayer)
conv = BatchNormalization(name = prefix + 'BatchNormalization')(conv)
conv = Activation('relu', name = prefix + 'ActivationLayer')(conv)
return conv
def create_model( ):
model = MobileNetV2(weights="imagenet", include_top=False, input_tensor = Input(shape=(224, 224, 3)))
for layer in model.layers :
layer.trainable = False
block1 = model.get_layer('out_relu').output
block2 = model.get_layer('block_7_add').output
block3 = model.get_layer('block_4_add').output
block4 = model.get_layer('block_2_add').output
block5 = model.get_layer('Conv1').output
up1 = Concatenate()([UpSampling2D()(block1), block2])
conv6 = create_conv_block(up1, 640, 'Conv_6_1')
conv6 = create_conv_block(conv6, 640, 'Conv_6_2')
up2 = Concatenate()([UpSampling2D()(conv6), block3])
conv7 = create_conv_block(up2, 256, 'Conv_7_1')
conv7 = create_conv_block(conv7, 256, 'Conv_7_2')
up3 = Concatenate()([UpSampling2D()(conv7), block4])
conv8 = create_conv_block(up3, 64, 'Conv_8_1')
conv8 = create_conv_block(conv8, 64, 'Conv_8_2')
up4 = Concatenate()([UpSampling2D()(conv8), block5])
conv9 = create_conv_block(up4, 32, 'Conv_9_1')
conv9 = create_conv_block(conv9, 32, 'Conv_9_2')
up5 = Concatenate()([UpSampling2D()(conv9), model.input])
conv10 = create_conv_block(up5, 16, 'Conv_10_1')
conv10 = create_conv_block(conv10, 16, 'Conv_10_2')
x = Conv2D(1, (1, 1), activation = 'sigmoid')(conv10)
x = Reshape((IMAGE_WIDTH, IMAGE_HEIGHT))(x)
return Model(inputs = model.input, outputs = x)
# Give trainable=False as argument, if you want to freeze lower layers for fast training (but low accuracy)
model = create_model()
model.summary()
Model: "model_3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_6 (InputLayer) [(None, 224, 224, 3 0 []
)]
Conv1 (Conv2D) (None, 112, 112, 32 864 ['input_6[0][0]']
)
bn_Conv1 (BatchNormalization) (None, 112, 112, 32 128 ['Conv1[0][0]']
)
Conv1_relu (ReLU) (None, 112, 112, 32 0 ['bn_Conv1[0][0]']
)
expanded_conv_depthwise (Depth (None, 112, 112, 32 288 ['Conv1_relu[0][0]']
wiseConv2D) )
expanded_conv_depthwise_BN (Ba (None, 112, 112, 32 128 ['expanded_conv_depthwise[0][0]']
tchNormalization) )
expanded_conv_depthwise_relu ( (None, 112, 112, 32 0 ['expanded_conv_depthwise_BN[0][0
ReLU) ) ]']
expanded_conv_project (Conv2D) (None, 112, 112, 16 512 ['expanded_conv_depthwise_relu[0]
) [0]']
expanded_conv_project_BN (Batc (None, 112, 112, 16 64 ['expanded_conv_project[0][0]']
hNormalization) )
block_1_expand (Conv2D) (None, 112, 112, 96 1536 ['expanded_conv_project_BN[0][0]'
) ]
block_1_expand_BN (BatchNormal (None, 112, 112, 96 384 ['block_1_expand[0][0]']
ization) )
block_1_expand_relu (ReLU) (None, 112, 112, 96 0 ['block_1_expand_BN[0][0]']
)
block_1_pad (ZeroPadding2D) (None, 113, 113, 96 0 ['block_1_expand_relu[0][0]']
)
block_1_depthwise (DepthwiseCo (None, 56, 56, 96) 864 ['block_1_pad[0][0]']
nv2D)
block_1_depthwise_BN (BatchNor (None, 56, 56, 96) 384 ['block_1_depthwise[0][0]']
malization)
block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 ['block_1_depthwise_BN[0][0]']
block_1_project (Conv2D) (None, 56, 56, 24) 2304 ['block_1_depthwise_relu[0][0]']
block_1_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_1_project[0][0]']
lization)
block_2_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_1_project_BN[0][0]']
block_2_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_2_expand[0][0]']
ization)
block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_expand_BN[0][0]']
block_2_depthwise (DepthwiseCo (None, 56, 56, 144) 1296 ['block_2_expand_relu[0][0]']
nv2D)
block_2_depthwise_BN (BatchNor (None, 56, 56, 144) 576 ['block_2_depthwise[0][0]']
malization)
block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_depthwise_BN[0][0]']
block_2_project (Conv2D) (None, 56, 56, 24) 3456 ['block_2_depthwise_relu[0][0]']
block_2_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_2_project[0][0]']
lization)
block_2_add (Add) (None, 56, 56, 24) 0 ['block_1_project_BN[0][0]',
'block_2_project_BN[0][0]']
block_3_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_2_add[0][0]']
block_3_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_3_expand[0][0]']
ization)
block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_3_expand_BN[0][0]']
block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 ['block_3_expand_relu[0][0]']
block_3_depthwise (DepthwiseCo (None, 28, 28, 144) 1296 ['block_3_pad[0][0]']
nv2D)
block_3_depthwise_BN (BatchNor (None, 28, 28, 144) 576 ['block_3_depthwise[0][0]']
malization)
block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 ['block_3_depthwise_BN[0][0]']
block_3_project (Conv2D) (None, 28, 28, 32) 4608 ['block_3_depthwise_relu[0][0]']
block_3_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_3_project[0][0]']
lization)
block_4_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_3_project_BN[0][0]']
block_4_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_4_expand[0][0]']
ization)
block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_expand_BN[0][0]']
block_4_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_4_expand_relu[0][0]']
nv2D)
block_4_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_4_depthwise[0][0]']
malization)
block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_depthwise_BN[0][0]']
block_4_project (Conv2D) (None, 28, 28, 32) 6144 ['block_4_depthwise_relu[0][0]']
block_4_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_4_project[0][0]']
lization)
block_4_add (Add) (None, 28, 28, 32) 0 ['block_3_project_BN[0][0]',
'block_4_project_BN[0][0]']
block_5_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_4_add[0][0]']
block_5_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_5_expand[0][0]']
ization)
block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_expand_BN[0][0]']
block_5_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_5_expand_relu[0][0]']
nv2D)
block_5_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_5_depthwise[0][0]']
malization)
block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_depthwise_BN[0][0]']
block_5_project (Conv2D) (None, 28, 28, 32) 6144 ['block_5_depthwise_relu[0][0]']
block_5_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_5_project[0][0]']
lization)
block_5_add (Add) (None, 28, 28, 32) 0 ['block_4_add[0][0]',
'block_5_project_BN[0][0]']
block_6_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_5_add[0][0]']
block_6_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_6_expand[0][0]']
ization)
block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_6_expand_BN[0][0]']
block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 ['block_6_expand_relu[0][0]']
block_6_depthwise (DepthwiseCo (None, 14, 14, 192) 1728 ['block_6_pad[0][0]']
nv2D)
block_6_depthwise_BN (BatchNor (None, 14, 14, 192) 768 ['block_6_depthwise[0][0]']
malization)
block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 ['block_6_depthwise_BN[0][0]']
block_6_project (Conv2D) (None, 14, 14, 64) 12288 ['block_6_depthwise_relu[0][0]']
block_6_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_6_project[0][0]']
lization)
block_7_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_6_project_BN[0][0]']
block_7_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_7_expand[0][0]']
ization)
block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_expand_BN[0][0]']
block_7_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_7_expand_relu[0][0]']
nv2D)
block_7_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_7_depthwise[0][0]']
malization)
block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_depthwise_BN[0][0]']
block_7_project (Conv2D) (None, 14, 14, 64) 24576 ['block_7_depthwise_relu[0][0]']
block_7_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_7_project[0][0]']
lization)
block_7_add (Add) (None, 14, 14, 64) 0 ['block_6_project_BN[0][0]',
'block_7_project_BN[0][0]']
block_8_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_7_add[0][0]']
block_8_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_8_expand[0][0]']
ization)
block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_expand_BN[0][0]']
block_8_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_8_expand_relu[0][0]']
nv2D)
block_8_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_8_depthwise[0][0]']
malization)
block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_depthwise_BN[0][0]']
block_8_project (Conv2D) (None, 14, 14, 64) 24576 ['block_8_depthwise_relu[0][0]']
block_8_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_8_project[0][0]']
lization)
block_8_add (Add) (None, 14, 14, 64) 0 ['block_7_add[0][0]',
'block_8_project_BN[0][0]']
block_9_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_8_add[0][0]']
block_9_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_9_expand[0][0]']
ization)
block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_expand_BN[0][0]']
block_9_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_9_expand_relu[0][0]']
nv2D)
block_9_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_9_depthwise[0][0]']
malization)
block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_depthwise_BN[0][0]']
block_9_project (Conv2D) (None, 14, 14, 64) 24576 ['block_9_depthwise_relu[0][0]']
block_9_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_9_project[0][0]']
lization)
block_9_add (Add) (None, 14, 14, 64) 0 ['block_8_add[0][0]',
'block_9_project_BN[0][0]']
block_10_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_9_add[0][0]']
block_10_expand_BN (BatchNorma (None, 14, 14, 384) 1536 ['block_10_expand[0][0]']
lization)
block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_expand_BN[0][0]']
block_10_depthwise (DepthwiseC (None, 14, 14, 384) 3456 ['block_10_expand_relu[0][0]']
onv2D)
block_10_depthwise_BN (BatchNo (None, 14, 14, 384) 1536 ['block_10_depthwise[0][0]']
rmalization)
block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_depthwise_BN[0][0]']
block_10_project (Conv2D) (None, 14, 14, 96) 36864 ['block_10_depthwise_relu[0][0]']
block_10_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_10_project[0][0]']
alization)
block_11_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_10_project_BN[0][0]']
block_11_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_11_expand[0][0]']
lization)
block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_expand_BN[0][0]']
block_11_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_11_expand_relu[0][0]']
onv2D)
block_11_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_11_depthwise[0][0]']
rmalization)
block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_depthwise_BN[0][0]']
block_11_project (Conv2D) (None, 14, 14, 96) 55296 ['block_11_depthwise_relu[0][0]']
block_11_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_11_project[0][0]']
alization)
block_11_add (Add) (None, 14, 14, 96) 0 ['block_10_project_BN[0][0]',
'block_11_project_BN[0][0]']
block_12_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_11_add[0][0]']
block_12_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_12_expand[0][0]']
lization)
block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_expand_BN[0][0]']
block_12_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_12_expand_relu[0][0]']
onv2D)
block_12_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_12_depthwise[0][0]']
rmalization)
block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_depthwise_BN[0][0]']
block_12_project (Conv2D) (None, 14, 14, 96) 55296 ['block_12_depthwise_relu[0][0]']
block_12_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_12_project[0][0]']
alization)
block_12_add (Add) (None, 14, 14, 96) 0 ['block_11_add[0][0]',
'block_12_project_BN[0][0]']
block_13_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_12_add[0][0]']
block_13_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_13_expand[0][0]']
lization)
block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_13_expand_BN[0][0]']
block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 ['block_13_expand_relu[0][0]']
block_13_depthwise (DepthwiseC (None, 7, 7, 576) 5184 ['block_13_pad[0][0]']
onv2D)
block_13_depthwise_BN (BatchNo (None, 7, 7, 576) 2304 ['block_13_depthwise[0][0]']
rmalization)
block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 ['block_13_depthwise_BN[0][0]']
block_13_project (Conv2D) (None, 7, 7, 160) 92160 ['block_13_depthwise_relu[0][0]']
block_13_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_13_project[0][0]']
alization)
block_14_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_13_project_BN[0][0]']
block_14_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_14_expand[0][0]']
lization)
block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_expand_BN[0][0]']
block_14_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_14_expand_relu[0][0]']
onv2D)
block_14_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_14_depthwise[0][0]']
rmalization)
block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_depthwise_BN[0][0]']
block_14_project (Conv2D) (None, 7, 7, 160) 153600 ['block_14_depthwise_relu[0][0]']
block_14_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_14_project[0][0]']
alization)
block_14_add (Add) (None, 7, 7, 160) 0 ['block_13_project_BN[0][0]',
'block_14_project_BN[0][0]']
block_15_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_14_add[0][0]']
block_15_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_15_expand[0][0]']
lization)
block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_expand_BN[0][0]']
block_15_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_15_expand_relu[0][0]']
onv2D)
block_15_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_15_depthwise[0][0]']
rmalization)
block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_depthwise_BN[0][0]']
block_15_project (Conv2D) (None, 7, 7, 160) 153600 ['block_15_depthwise_relu[0][0]']
block_15_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_15_project[0][0]']
alization)
block_15_add (Add) (None, 7, 7, 160) 0 ['block_14_add[0][0]',
'block_15_project_BN[0][0]']
block_16_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_15_add[0][0]']
block_16_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_16_expand[0][0]']
lization)
block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_expand_BN[0][0]']
block_16_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_16_expand_relu[0][0]']
onv2D)
block_16_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_16_depthwise[0][0]']
rmalization)
block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_depthwise_BN[0][0]']
block_16_project (Conv2D) (None, 7, 7, 320) 307200 ['block_16_depthwise_relu[0][0]']
block_16_project_BN (BatchNorm (None, 7, 7, 320) 1280 ['block_16_project[0][0]']
alization)
Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 ['block_16_project_BN[0][0]']
Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['Conv_1[0][0]']
out_relu (ReLU) (None, 7, 7, 1280) 0 ['Conv_1_bn[0][0]']
up_sampling2d_16 (UpSampling2D (None, 14, 14, 1280 0 ['out_relu[0][0]']
) )
concatenate_16 (Concatenate) (None, 14, 14, 1344 0 ['up_sampling2d_16[0][0]',
) 'block_7_add[0][0]']
Conv_6_1_conv (Conv2D) (None, 14, 14, 640) 7742080 ['concatenate_16[0][0]']
Conv_6_1BatchNormalization (Ba (None, 14, 14, 640) 2560 ['Conv_6_1_conv[0][0]']
tchNormalization)
Conv_6_1ActivationLayer (Activ (None, 14, 14, 640) 0 ['Conv_6_1BatchNormalization[0][0
ation) ]']
Conv_6_2_conv (Conv2D) (None, 14, 14, 640) 3687040 ['Conv_6_1ActivationLayer[0][0]']
Conv_6_2BatchNormalization (Ba (None, 14, 14, 640) 2560 ['Conv_6_2_conv[0][0]']
tchNormalization)
Conv_6_2ActivationLayer (Activ (None, 14, 14, 640) 0 ['Conv_6_2BatchNormalization[0][0
ation) ]']
up_sampling2d_17 (UpSampling2D (None, 28, 28, 640) 0 ['Conv_6_2ActivationLayer[0][0]']
)
concatenate_17 (Concatenate) (None, 28, 28, 672) 0 ['up_sampling2d_17[0][0]',
'block_4_add[0][0]']
Conv_7_1_conv (Conv2D) (None, 28, 28, 256) 1548544 ['concatenate_17[0][0]']
Conv_7_1BatchNormalization (Ba (None, 28, 28, 256) 1024 ['Conv_7_1_conv[0][0]']
tchNormalization)
Conv_7_1ActivationLayer (Activ (None, 28, 28, 256) 0 ['Conv_7_1BatchNormalization[0][0
ation) ]']
Conv_7_2_conv (Conv2D) (None, 28, 28, 256) 590080 ['Conv_7_1ActivationLayer[0][0]']
Conv_7_2BatchNormalization (Ba (None, 28, 28, 256) 1024 ['Conv_7_2_conv[0][0]']
tchNormalization)
Conv_7_2ActivationLayer (Activ (None, 28, 28, 256) 0 ['Conv_7_2BatchNormalization[0][0
ation) ]']
up_sampling2d_18 (UpSampling2D (None, 56, 56, 256) 0 ['Conv_7_2ActivationLayer[0][0]']
)
concatenate_18 (Concatenate) (None, 56, 56, 280) 0 ['up_sampling2d_18[0][0]',
'block_2_add[0][0]']
Conv_8_1_conv (Conv2D) (None, 56, 56, 64) 161344 ['concatenate_18[0][0]']
Conv_8_1BatchNormalization (Ba (None, 56, 56, 64) 256 ['Conv_8_1_conv[0][0]']
tchNormalization)
Conv_8_1ActivationLayer (Activ (None, 56, 56, 64) 0 ['Conv_8_1BatchNormalization[0][0
ation) ]']
Conv_8_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['Conv_8_1ActivationLayer[0][0]']
Conv_8_2BatchNormalization (Ba (None, 56, 56, 64) 256 ['Conv_8_2_conv[0][0]']
tchNormalization)
Conv_8_2ActivationLayer (Activ (None, 56, 56, 64) 0 ['Conv_8_2BatchNormalization[0][0
ation) ]']
up_sampling2d_19 (UpSampling2D (None, 112, 112, 64 0 ['Conv_8_2ActivationLayer[0][0]']
) )
concatenate_19 (Concatenate) (None, 112, 112, 96 0 ['up_sampling2d_19[0][0]',
) 'Conv1[0][0]']
Conv_9_1_conv (Conv2D) (None, 112, 112, 32 27680 ['concatenate_19[0][0]']
)
Conv_9_1BatchNormalization (Ba (None, 112, 112, 32 128 ['Conv_9_1_conv[0][0]']
tchNormalization) )
Conv_9_1ActivationLayer (Activ (None, 112, 112, 32 0 ['Conv_9_1BatchNormalization[0][0
ation) ) ]']
Conv_9_2_conv (Conv2D) (None, 112, 112, 32 9248 ['Conv_9_1ActivationLayer[0][0]']
)
Conv_9_2BatchNormalization (Ba (None, 112, 112, 32 128 ['Conv_9_2_conv[0][0]']
tchNormalization) )
Conv_9_2ActivationLayer (Activ (None, 112, 112, 32 0 ['Conv_9_2BatchNormalization[0][0
ation) ) ]']
up_sampling2d_20 (UpSampling2D (None, 224, 224, 32 0 ['Conv_9_2ActivationLayer[0][0]']
) )
concatenate_20 (Concatenate) (None, 224, 224, 35 0 ['up_sampling2d_20[0][0]',
) 'input_6[0][0]']
Conv_10_1_conv (Conv2D) (None, 224, 224, 16 5056 ['concatenate_20[0][0]']
)
Conv_10_1BatchNormalization (B (None, 224, 224, 16 64 ['Conv_10_1_conv[0][0]']
atchNormalization) )
Conv_10_1ActivationLayer (Acti (None, 224, 224, 16 0 ['Conv_10_1BatchNormalization[0][
vation) ) 0]']
Conv_10_2_conv (Conv2D) (None, 224, 224, 16 2320 ['Conv_10_1ActivationLayer[0][0]'
) ]
Conv_10_2BatchNormalization (B (None, 224, 224, 16 64 ['Conv_10_2_conv[0][0]']
atchNormalization) )
Conv_10_2ActivationLayer (Acti (None, 224, 224, 16 0 ['Conv_10_2BatchNormalization[0][
vation) ) 0]']
conv2d_3 (Conv2D) (None, 224, 224, 1) 17 ['Conv_10_2ActivationLayer[0][0]'
]
reshape_3 (Reshape) (None, 224, 224) 0 ['conv2d_3[0][0]']
==================================================================================================
Total params: 16,076,385
Trainable params: 13,814,369
Non-trainable params: 2,262,016
__________________________________________________________________________________________________
2B.Design your own Dice Coefficient and Loss function.
def dice_coefficient(y_true, y_pred):
numerator = 2 * tf.reduce_sum(y_true * y_pred)
denominator = tf.reduce_sum(y_true + y_pred)
return numerator / (denominator + tf.keras.backend.epsilon())
from keras.losses import binary_crossentropy
from keras.backend import log, epsilon
def loss(y_true, y_pred):
return binary_crossentropy(y_true, y_pred) - log(dice_coefficient(y_true, y_pred) + epsilon())
2C.Train and tune the model as required.
from keras.optimizers import Adam
optimizer = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss=loss, optimizer=optimizer, metrics=[dice_coefficient])
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
checkpoint = ModelCheckpoint('model_{loss:.2f}.h5', monitor = 'loss', verbose = 1, save_best_only = True, save_weights_only = True, mode = 'min', save_freq = 1)
stop = EarlyStopping(monitor="val_loss", patience=5, mode="min")
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.1, patience=3, min_lr=1e-6, verbose=1, mode="min")
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
(400, 224, 224, 3) (400, 224, 224) (9, 224, 224, 3) (9, 224, 224)
model.fit(X_train, y_train, epochs = 10, batch_size =1, callbacks = [checkpoint, reduce_lr, stop], validation_data = (X_test, y_test))
Streaming output truncated to the last 5000 lines.
307/400 [======================>.......] - ETA: 1:48 - loss: 0.8370 - dice_coefficient: 0.5737
Epoch 4: loss did not improve from 0.55412
308/400 [======================>.......] - ETA: 1:47 - loss: 0.8356 - dice_coefficient: 0.5743
Epoch 4: loss did not improve from 0.55412
309/400 [======================>.......] - ETA: 1:45 - loss: 0.8359 - dice_coefficient: 0.5739
Epoch 4: loss did not improve from 0.55412
310/400 [======================>.......] - ETA: 1:44 - loss: 0.8365 - dice_coefficient: 0.5733
Epoch 4: loss did not improve from 0.55412
311/400 [======================>.......] - ETA: 1:43 - loss: 0.8359 - dice_coefficient: 0.5734
Epoch 4: loss did not improve from 0.55412
312/400 [======================>.......] - ETA: 1:42 - loss: 0.8347 - dice_coefficient: 0.5738
Epoch 4: loss did not improve from 0.55412
313/400 [======================>.......] - ETA: 1:41 - loss: 0.8343 - dice_coefficient: 0.5739
Epoch 4: loss did not improve from 0.55412
314/400 [======================>.......] - ETA: 1:39 - loss: 0.8332 - dice_coefficient: 0.5744
Epoch 4: loss did not improve from 0.55412
315/400 [======================>.......] - ETA: 1:38 - loss: 0.8319 - dice_coefficient: 0.5751
Epoch 4: loss did not improve from 0.55412
316/400 [======================>.......] - ETA: 1:37 - loss: 0.8308 - dice_coefficient: 0.5755
Epoch 4: loss did not improve from 0.55412
317/400 [======================>.......] - ETA: 1:36 - loss: 0.8311 - dice_coefficient: 0.5751
Epoch 4: loss did not improve from 0.55412
318/400 [======================>.......] - ETA: 1:35 - loss: 0.8307 - dice_coefficient: 0.5750
Epoch 4: loss did not improve from 0.55412
319/400 [======================>.......] - ETA: 1:34 - loss: 0.8319 - dice_coefficient: 0.5742
Epoch 4: loss did not improve from 0.55412
320/400 [=======================>......] - ETA: 1:33 - loss: 0.8314 - dice_coefficient: 0.5742
Epoch 4: loss did not improve from 0.55412
321/400 [=======================>......] - ETA: 1:31 - loss: 0.8304 - dice_coefficient: 0.5746
Epoch 4: loss did not improve from 0.55412
322/400 [=======================>......] - ETA: 1:30 - loss: 0.8293 - dice_coefficient: 0.5751
Epoch 4: loss did not improve from 0.55412
323/400 [=======================>......] - ETA: 1:29 - loss: 0.8278 - dice_coefficient: 0.5759
Epoch 4: loss did not improve from 0.55412
324/400 [=======================>......] - ETA: 1:28 - loss: 0.8291 - dice_coefficient: 0.5752
Epoch 4: loss did not improve from 0.55412
325/400 [=======================>......] - ETA: 1:27 - loss: 0.8284 - dice_coefficient: 0.5752
Epoch 4: loss did not improve from 0.55412
326/400 [=======================>......] - ETA: 1:25 - loss: 0.8282 - dice_coefficient: 0.5751
Epoch 4: loss did not improve from 0.55412
327/400 [=======================>......] - ETA: 1:24 - loss: 0.8268 - dice_coefficient: 0.5758
Epoch 4: loss did not improve from 0.55412
328/400 [=======================>......] - ETA: 1:23 - loss: 0.8260 - dice_coefficient: 0.5760
Epoch 4: loss did not improve from 0.55412
329/400 [=======================>......] - ETA: 1:22 - loss: 0.8247 - dice_coefficient: 0.5765
Epoch 4: loss did not improve from 0.55412
330/400 [=======================>......] - ETA: 1:21 - loss: 0.8258 - dice_coefficient: 0.5759
Epoch 4: loss did not improve from 0.55412
331/400 [=======================>......] - ETA: 1:20 - loss: 0.8290 - dice_coefficient: 0.5747
Epoch 4: loss did not improve from 0.55412
332/400 [=======================>......] - ETA: 1:19 - loss: 0.8276 - dice_coefficient: 0.5754
Epoch 4: loss did not improve from 0.55412
333/400 [=======================>......] - ETA: 1:17 - loss: 0.8263 - dice_coefficient: 0.5761
Epoch 4: loss did not improve from 0.55412
334/400 [========================>.....] - ETA: 1:16 - loss: 0.8268 - dice_coefficient: 0.5756
Epoch 4: loss did not improve from 0.55412
335/400 [========================>.....] - ETA: 1:15 - loss: 0.8312 - dice_coefficient: 0.5743
Epoch 4: loss did not improve from 0.55412
336/400 [========================>.....] - ETA: 1:14 - loss: 0.8300 - dice_coefficient: 0.5748
Epoch 4: loss did not improve from 0.55412
337/400 [========================>.....] - ETA: 1:13 - loss: 0.8295 - dice_coefficient: 0.5748
Epoch 4: loss did not improve from 0.55412
338/400 [========================>.....] - ETA: 1:11 - loss: 0.8287 - dice_coefficient: 0.5750
Epoch 4: loss did not improve from 0.55412
339/400 [========================>.....] - ETA: 1:10 - loss: 0.8296 - dice_coefficient: 0.5744
Epoch 4: loss did not improve from 0.55412
340/400 [========================>.....] - ETA: 1:09 - loss: 0.8290 - dice_coefficient: 0.5744
Epoch 4: loss did not improve from 0.55412
341/400 [========================>.....] - ETA: 1:08 - loss: 0.8314 - dice_coefficient: 0.5734
Epoch 4: loss did not improve from 0.55412
342/400 [========================>.....] - ETA: 1:07 - loss: 0.8301 - dice_coefficient: 0.5740
Epoch 4: loss did not improve from 0.55412
343/400 [========================>.....] - ETA: 1:06 - loss: 0.8291 - dice_coefficient: 0.5746
Epoch 4: loss did not improve from 0.55412
344/400 [========================>.....] - ETA: 1:05 - loss: 0.8286 - dice_coefficient: 0.5747
Epoch 4: loss did not improve from 0.55412
345/400 [========================>.....] - ETA: 1:03 - loss: 0.8294 - dice_coefficient: 0.5741
Epoch 4: loss did not improve from 0.55412
346/400 [========================>.....] - ETA: 1:02 - loss: 0.8285 - dice_coefficient: 0.5743
Epoch 4: loss did not improve from 0.55412
347/400 [=========================>....] - ETA: 1:01 - loss: 0.8286 - dice_coefficient: 0.5740
Epoch 4: loss did not improve from 0.55412
348/400 [=========================>....] - ETA: 1:00 - loss: 0.8278 - dice_coefficient: 0.5746
Epoch 4: loss did not improve from 0.55412
349/400 [=========================>....] - ETA: 59s - loss: 0.8263 - dice_coefficient: 0.5753
Epoch 4: loss did not improve from 0.55412
350/400 [=========================>....] - ETA: 57s - loss: 0.8254 - dice_coefficient: 0.5756
Epoch 4: loss did not improve from 0.55412
351/400 [=========================>....] - ETA: 56s - loss: 0.8245 - dice_coefficient: 0.5759
Epoch 4: loss did not improve from 0.55412
352/400 [=========================>....] - ETA: 55s - loss: 0.8236 - dice_coefficient: 0.5761
Epoch 4: loss did not improve from 0.55412
353/400 [=========================>....] - ETA: 54s - loss: 0.8222 - dice_coefficient: 0.5768
Epoch 4: loss did not improve from 0.55412
354/400 [=========================>....] - ETA: 53s - loss: 0.8210 - dice_coefficient: 0.5773
Epoch 4: loss did not improve from 0.55412
355/400 [=========================>....] - ETA: 52s - loss: 0.8199 - dice_coefficient: 0.5777
Epoch 4: loss did not improve from 0.55412
356/400 [=========================>....] - ETA: 51s - loss: 0.8188 - dice_coefficient: 0.5781
Epoch 4: loss did not improve from 0.55412
357/400 [=========================>....] - ETA: 49s - loss: 0.8174 - dice_coefficient: 0.5787
Epoch 4: loss did not improve from 0.55412
358/400 [=========================>....] - ETA: 48s - loss: 0.8167 - dice_coefficient: 0.5792
Epoch 4: loss did not improve from 0.55412
359/400 [=========================>....] - ETA: 47s - loss: 0.8161 - dice_coefficient: 0.5797
Epoch 4: loss did not improve from 0.55412
360/400 [==========================>...] - ETA: 46s - loss: 0.8157 - dice_coefficient: 0.5798
Epoch 4: loss did not improve from 0.55412
361/400 [==========================>...] - ETA: 45s - loss: 0.8153 - dice_coefficient: 0.5798
Epoch 4: loss did not improve from 0.55412
362/400 [==========================>...] - ETA: 43s - loss: 0.8140 - dice_coefficient: 0.5804
Epoch 4: loss did not improve from 0.55412
363/400 [==========================>...] - ETA: 42s - loss: 0.8127 - dice_coefficient: 0.5811
Epoch 4: loss did not improve from 0.55412
364/400 [==========================>...] - ETA: 41s - loss: 0.8125 - dice_coefficient: 0.5810
Epoch 4: loss did not improve from 0.55412
365/400 [==========================>...] - ETA: 40s - loss: 0.8115 - dice_coefficient: 0.5814
Epoch 4: loss did not improve from 0.55412
366/400 [==========================>...] - ETA: 39s - loss: 0.8107 - dice_coefficient: 0.5816
Epoch 4: loss did not improve from 0.55412
367/400 [==========================>...] - ETA: 38s - loss: 0.8092 - dice_coefficient: 0.5823
Epoch 4: loss did not improve from 0.55412
368/400 [==========================>...] - ETA: 37s - loss: 0.8084 - dice_coefficient: 0.5826
Epoch 4: loss did not improve from 0.55412
369/400 [==========================>...] - ETA: 35s - loss: 0.8076 - dice_coefficient: 0.5828
Epoch 4: loss did not improve from 0.55412
370/400 [==========================>...] - ETA: 34s - loss: 0.8070 - dice_coefficient: 0.5829
Epoch 4: loss did not improve from 0.55412
371/400 [==========================>...] - ETA: 33s - loss: 0.8073 - dice_coefficient: 0.5826
Epoch 4: loss did not improve from 0.55412
372/400 [==========================>...] - ETA: 32s - loss: 0.8071 - dice_coefficient: 0.5827
Epoch 4: loss did not improve from 0.55412
373/400 [==========================>...] - ETA: 31s - loss: 0.8062 - dice_coefficient: 0.5829
Epoch 4: loss did not improve from 0.55412
374/400 [===========================>..] - ETA: 30s - loss: 0.8058 - dice_coefficient: 0.5829
Epoch 4: loss did not improve from 0.55412
375/400 [===========================>..] - ETA: 28s - loss: 0.8048 - dice_coefficient: 0.5833
Epoch 4: loss did not improve from 0.55412
376/400 [===========================>..] - ETA: 27s - loss: 0.8062 - dice_coefficient: 0.5835
Epoch 4: loss did not improve from 0.55412
377/400 [===========================>..] - ETA: 26s - loss: 0.8059 - dice_coefficient: 0.5834
Epoch 4: loss did not improve from 0.55412
378/400 [===========================>..] - ETA: 25s - loss: 0.8049 - dice_coefficient: 0.5838
Epoch 4: loss did not improve from 0.55412
379/400 [===========================>..] - ETA: 24s - loss: 0.8047 - dice_coefficient: 0.5841
Epoch 4: loss did not improve from 0.55412
380/400 [===========================>..] - ETA: 23s - loss: 0.8041 - dice_coefficient: 0.5842
Epoch 4: loss did not improve from 0.55412
381/400 [===========================>..] - ETA: 22s - loss: 0.8036 - dice_coefficient: 0.5844
Epoch 4: loss did not improve from 0.55412
382/400 [===========================>..] - ETA: 20s - loss: 0.8040 - dice_coefficient: 0.5841
Epoch 4: loss did not improve from 0.55412
383/400 [===========================>..] - ETA: 19s - loss: 0.8031 - dice_coefficient: 0.5845
Epoch 4: loss did not improve from 0.55412
384/400 [===========================>..] - ETA: 18s - loss: 0.8021 - dice_coefficient: 0.5850
Epoch 4: loss did not improve from 0.55412
385/400 [===========================>..] - ETA: 17s - loss: 0.8020 - dice_coefficient: 0.5852
Epoch 4: loss did not improve from 0.55412
386/400 [===========================>..] - ETA: 16s - loss: 0.8014 - dice_coefficient: 0.5857
Epoch 4: loss did not improve from 0.55412
387/400 [============================>.] - ETA: 15s - loss: 0.8004 - dice_coefficient: 0.5861
Epoch 4: loss did not improve from 0.55412
388/400 [============================>.] - ETA: 13s - loss: 0.8013 - dice_coefficient: 0.5855
Epoch 4: loss did not improve from 0.55412
389/400 [============================>.] - ETA: 12s - loss: 0.8000 - dice_coefficient: 0.5861
Epoch 4: loss did not improve from 0.55412
390/400 [============================>.] - ETA: 11s - loss: 0.7991 - dice_coefficient: 0.5865
Epoch 4: loss did not improve from 0.55412
391/400 [============================>.] - ETA: 10s - loss: 0.7997 - dice_coefficient: 0.5860
Epoch 4: loss did not improve from 0.55412
392/400 [============================>.] - ETA: 9s - loss: 0.7990 - dice_coefficient: 0.5862
Epoch 4: loss did not improve from 0.55412
393/400 [============================>.] - ETA: 8s - loss: 0.7983 - dice_coefficient: 0.5864
Epoch 4: loss did not improve from 0.55412
394/400 [============================>.] - ETA: 6s - loss: 0.7975 - dice_coefficient: 0.5868
Epoch 4: loss did not improve from 0.55412
395/400 [============================>.] - ETA: 5s - loss: 0.7975 - dice_coefficient: 0.5866
Epoch 4: loss did not improve from 0.55412
396/400 [============================>.] - ETA: 4s - loss: 0.7968 - dice_coefficient: 0.5869
Epoch 4: loss did not improve from 0.55412
397/400 [============================>.] - ETA: 3s - loss: 0.7969 - dice_coefficient: 0.5867
Epoch 4: loss did not improve from 0.55412
398/400 [============================>.] - ETA: 2s - loss: 0.7971 - dice_coefficient: 0.5870
Epoch 4: loss did not improve from 0.55412
399/400 [============================>.] - ETA: 1s - loss: 0.7964 - dice_coefficient: 0.5873
Epoch 4: loss did not improve from 0.55412
400/400 [==============================] - 468s 1s/step - loss: 0.7954 - dice_coefficient: 0.5877 - val_loss: 1.0532 - val_dice_coefficient: 0.4742 - lr: 1.0000e-04
Epoch 5/10
Epoch 5: loss improved from 0.55412 to 0.33900, saving model to model_0.34.h5
1/400 [..............................] - ETA: 8:42 - loss: 0.3390 - dice_coefficient: 0.7776
Epoch 5: loss did not improve from 0.33900
2/400 [..............................] - ETA: 6:41 - loss: 0.4703 - dice_coefficient: 0.6943
Epoch 5: loss did not improve from 0.33900
3/400 [..............................] - ETA: 6:25 - loss: 0.5656 - dice_coefficient: 0.6361
Epoch 5: loss did not improve from 0.33900
4/400 [..............................] - ETA: 6:19 - loss: 0.6310 - dice_coefficient: 0.6070
Epoch 5: loss did not improve from 0.33900
5/400 [..............................] - ETA: 6:16 - loss: 0.7115 - dice_coefficient: 0.5636
Epoch 5: loss did not improve from 0.33900
6/400 [..............................] - ETA: 6:11 - loss: 0.6688 - dice_coefficient: 0.5971
Epoch 5: loss did not improve from 0.33900
7/400 [..............................] - ETA: 6:09 - loss: 0.6979 - dice_coefficient: 0.5813
Epoch 5: loss did not improve from 0.33900
8/400 [..............................] - ETA: 6:09 - loss: 0.6654 - dice_coefficient: 0.6004
Epoch 5: loss did not improve from 0.33900
9/400 [..............................] - ETA: 6:31 - loss: 0.6541 - dice_coefficient: 0.6052
Epoch 5: loss did not improve from 0.33900
10/400 [..............................] - ETA: 7:03 - loss: 0.6576 - dice_coefficient: 0.6201
Epoch 5: loss did not improve from 0.33900
11/400 [..............................] - ETA: 7:20 - loss: 0.6610 - dice_coefficient: 0.6145
Epoch 5: loss did not improve from 0.33900
12/400 [..............................] - ETA: 7:14 - loss: 0.6769 - dice_coefficient: 0.6029
Epoch 5: loss did not improve from 0.33900
13/400 [..............................] - ETA: 7:05 - loss: 0.7412 - dice_coefficient: 0.5764
Epoch 5: loss did not improve from 0.33900
14/400 [>.............................] - ETA: 7:02 - loss: 0.7513 - dice_coefficient: 0.5682
Epoch 5: loss did not improve from 0.33900
15/400 [>.............................] - ETA: 6:58 - loss: 0.7387 - dice_coefficient: 0.5737
Epoch 5: loss did not improve from 0.33900
16/400 [>.............................] - ETA: 6:55 - loss: 0.7555 - dice_coefficient: 0.5633
Epoch 5: loss did not improve from 0.33900
17/400 [>.............................] - ETA: 6:53 - loss: 0.7345 - dice_coefficient: 0.5736
Epoch 5: loss did not improve from 0.33900
18/400 [>.............................] - ETA: 6:52 - loss: 0.7591 - dice_coefficient: 0.5608
Epoch 5: loss did not improve from 0.33900
19/400 [>.............................] - ETA: 6:50 - loss: 0.7360 - dice_coefficient: 0.5738
Epoch 5: loss did not improve from 0.33900
20/400 [>.............................] - ETA: 6:48 - loss: 0.7130 - dice_coefficient: 0.5862
Epoch 5: loss did not improve from 0.33900
21/400 [>.............................] - ETA: 6:49 - loss: 0.6925 - dice_coefficient: 0.5976
Epoch 5: loss did not improve from 0.33900
22/400 [>.............................] - ETA: 7:00 - loss: 0.6766 - dice_coefficient: 0.6054
Epoch 5: loss did not improve from 0.33900
23/400 [>.............................] - ETA: 7:05 - loss: 0.6610 - dice_coefficient: 0.6134
Epoch 5: loss did not improve from 0.33900
24/400 [>.............................] - ETA: 7:01 - loss: 0.6506 - dice_coefficient: 0.6182
Epoch 5: loss did not improve from 0.33900
25/400 [>.............................] - ETA: 6:58 - loss: 0.6643 - dice_coefficient: 0.6095
Epoch 5: loss did not improve from 0.33900
26/400 [>.............................] - ETA: 6:55 - loss: 0.7043 - dice_coefficient: 0.5939
Epoch 5: loss did not improve from 0.33900
27/400 [=>............................] - ETA: 6:52 - loss: 0.6899 - dice_coefficient: 0.6016
Epoch 5: loss did not improve from 0.33900
28/400 [=>............................] - ETA: 6:49 - loss: 0.6816 - dice_coefficient: 0.6050
Epoch 5: loss did not improve from 0.33900
29/400 [=>............................] - ETA: 6:46 - loss: 0.6709 - dice_coefficient: 0.6102
Epoch 5: loss did not improve from 0.33900
30/400 [=>............................] - ETA: 6:44 - loss: 0.6600 - dice_coefficient: 0.6154
Epoch 5: loss did not improve from 0.33900
31/400 [=>............................] - ETA: 6:41 - loss: 0.6653 - dice_coefficient: 0.6109
Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
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Epoch 5: loss did not improve from 0.33900
359/400 [=========================>....] - ETA: 46s - loss: 0.6492 - dice_coefficient: 0.6487
Epoch 5: loss did not improve from 0.33900
360/400 [==========================>...] - ETA: 45s - loss: 0.6501 - dice_coefficient: 0.6480
Epoch 5: loss did not improve from 0.33900
361/400 [==========================>...] - ETA: 44s - loss: 0.6496 - dice_coefficient: 0.6482
Epoch 5: loss did not improve from 0.33900
362/400 [==========================>...] - ETA: 43s - loss: 0.6486 - dice_coefficient: 0.6486
Epoch 5: loss did not improve from 0.33900
363/400 [==========================>...] - ETA: 42s - loss: 0.6479 - dice_coefficient: 0.6490
Epoch 5: loss did not improve from 0.33900
364/400 [==========================>...] - ETA: 41s - loss: 0.6485 - dice_coefficient: 0.6485
Epoch 5: loss did not improve from 0.33900
365/400 [==========================>...] - ETA: 39s - loss: 0.6476 - dice_coefficient: 0.6490
Epoch 5: loss did not improve from 0.33900
366/400 [==========================>...] - ETA: 38s - loss: 0.6467 - dice_coefficient: 0.6494
Epoch 5: loss did not improve from 0.33900
367/400 [==========================>...] - ETA: 37s - loss: 0.6458 - dice_coefficient: 0.6499
Epoch 5: loss did not improve from 0.33900
368/400 [==========================>...] - ETA: 36s - loss: 0.6458 - dice_coefficient: 0.6497
Epoch 5: loss did not improve from 0.33900
369/400 [==========================>...] - ETA: 35s - loss: 0.6461 - dice_coefficient: 0.6494
Epoch 5: loss did not improve from 0.33900
370/400 [==========================>...] - ETA: 34s - loss: 0.6450 - dice_coefficient: 0.6500
Epoch 5: loss did not improve from 0.33900
371/400 [==========================>...] - ETA: 33s - loss: 0.6442 - dice_coefficient: 0.6504
Epoch 5: loss did not improve from 0.33900
372/400 [==========================>...] - ETA: 32s - loss: 0.6456 - dice_coefficient: 0.6497
Epoch 5: loss did not improve from 0.33900
373/400 [==========================>...] - ETA: 30s - loss: 0.6454 - dice_coefficient: 0.6496
Epoch 5: loss did not improve from 0.33900
374/400 [===========================>..] - ETA: 29s - loss: 0.6444 - dice_coefficient: 0.6501
Epoch 5: loss did not improve from 0.33900
375/400 [===========================>..] - ETA: 28s - loss: 0.6435 - dice_coefficient: 0.6505
Epoch 5: loss did not improve from 0.33900
376/400 [===========================>..] - ETA: 27s - loss: 0.6454 - dice_coefficient: 0.6495
Epoch 5: loss did not improve from 0.33900
377/400 [===========================>..] - ETA: 26s - loss: 0.6444 - dice_coefficient: 0.6499
Epoch 5: loss did not improve from 0.33900
378/400 [===========================>..] - ETA: 25s - loss: 0.6443 - dice_coefficient: 0.6498
Epoch 5: loss did not improve from 0.33900
379/400 [===========================>..] - ETA: 23s - loss: 0.6435 - dice_coefficient: 0.6501
Epoch 5: loss did not improve from 0.33900
380/400 [===========================>..] - ETA: 22s - loss: 0.6435 - dice_coefficient: 0.6500
Epoch 5: loss did not improve from 0.33900
381/400 [===========================>..] - ETA: 21s - loss: 0.6429 - dice_coefficient: 0.6501
Epoch 5: loss did not improve from 0.33900
382/400 [===========================>..] - ETA: 20s - loss: 0.6429 - dice_coefficient: 0.6499
Epoch 5: loss did not improve from 0.33900
383/400 [===========================>..] - ETA: 19s - loss: 0.6422 - dice_coefficient: 0.6503
Epoch 5: loss did not improve from 0.33900
384/400 [===========================>..] - ETA: 18s - loss: 0.6418 - dice_coefficient: 0.6504
Epoch 5: loss did not improve from 0.33900
385/400 [===========================>..] - ETA: 17s - loss: 0.6412 - dice_coefficient: 0.6506
Epoch 5: loss did not improve from 0.33900
386/400 [===========================>..] - ETA: 16s - loss: 0.6403 - dice_coefficient: 0.6510
Epoch 5: loss did not improve from 0.33900
387/400 [============================>.] - ETA: 14s - loss: 0.6398 - dice_coefficient: 0.6511
Epoch 5: loss did not improve from 0.33900
388/400 [============================>.] - ETA: 13s - loss: 0.6387 - dice_coefficient: 0.6516
Epoch 5: loss did not improve from 0.33900
389/400 [============================>.] - ETA: 12s - loss: 0.6377 - dice_coefficient: 0.6521
Epoch 5: loss did not improve from 0.33900
390/400 [============================>.] - ETA: 11s - loss: 0.6366 - dice_coefficient: 0.6526
Epoch 5: loss did not improve from 0.33900
391/400 [============================>.] - ETA: 10s - loss: 0.6359 - dice_coefficient: 0.6529
Epoch 5: loss did not improve from 0.33900
392/400 [============================>.] - ETA: 9s - loss: 0.6351 - dice_coefficient: 0.6533
Epoch 5: loss did not improve from 0.33900
393/400 [============================>.] - ETA: 7s - loss: 0.6358 - dice_coefficient: 0.6527
Epoch 5: loss did not improve from 0.33900
394/400 [============================>.] - ETA: 6s - loss: 0.6363 - dice_coefficient: 0.6522
Epoch 5: loss did not improve from 0.33900
395/400 [============================>.] - ETA: 5s - loss: 0.6358 - dice_coefficient: 0.6525
Epoch 5: loss did not improve from 0.33900
396/400 [============================>.] - ETA: 4s - loss: 0.6359 - dice_coefficient: 0.6523
Epoch 5: loss did not improve from 0.33900
397/400 [============================>.] - ETA: 3s - loss: 0.6351 - dice_coefficient: 0.6527
Epoch 5: loss did not improve from 0.33900
398/400 [============================>.] - ETA: 2s - loss: 0.6368 - dice_coefficient: 0.6518
Epoch 5: loss did not improve from 0.33900
399/400 [============================>.] - ETA: 1s - loss: 0.6368 - dice_coefficient: 0.6517
Epoch 5: loss did not improve from 0.33900
400/400 [==============================] - 459s 1s/step - loss: 0.6358 - dice_coefficient: 0.6523 - val_loss: 1.1454 - val_dice_coefficient: 0.4757 - lr: 1.0000e-04
Epoch 6/10
Epoch 6: loss improved from 0.33900 to 0.24339, saving model to model_0.24.h5
1/400 [..............................] - ETA: 9:06 - loss: 0.2434 - dice_coefficient: 0.8628
Epoch 6: loss did not improve from 0.24339
2/400 [..............................] - ETA: 6:54 - loss: 0.3051 - dice_coefficient: 0.8148
Epoch 6: loss did not improve from 0.24339
3/400 [..............................] - ETA: 9:02 - loss: 0.8101 - dice_coefficient: 0.6029
Epoch 6: loss did not improve from 0.24339
4/400 [..............................] - ETA: 9:41 - loss: 0.7419 - dice_coefficient: 0.6076
Epoch 6: loss did not improve from 0.24339
5/400 [..............................] - ETA: 9:07 - loss: 0.6561 - dice_coefficient: 0.6433
Epoch 6: loss did not improve from 0.24339
6/400 [..............................] - ETA: 8:39 - loss: 0.7108 - dice_coefficient: 0.6026
Epoch 6: loss did not improve from 0.24339
7/400 [..............................] - ETA: 8:25 - loss: 0.6905 - dice_coefficient: 0.6063
Epoch 6: loss did not improve from 0.24339
8/400 [..............................] - ETA: 8:06 - loss: 0.6582 - dice_coefficient: 0.6237
Epoch 6: loss did not improve from 0.24339
9/400 [..............................] - ETA: 7:56 - loss: 0.6095 - dice_coefficient: 0.6516
Epoch 6: loss did not improve from 0.24339
10/400 [..............................] - ETA: 7:47 - loss: 0.6096 - dice_coefficient: 0.6449
Epoch 6: loss did not improve from 0.24339
11/400 [..............................] - ETA: 7:38 - loss: 0.5874 - dice_coefficient: 0.6563
Epoch 6: loss did not improve from 0.24339
12/400 [..............................] - ETA: 7:29 - loss: 0.6253 - dice_coefficient: 0.6337
Epoch 6: loss did not improve from 0.24339
13/400 [..............................] - ETA: 7:23 - loss: 0.6205 - dice_coefficient: 0.6338
Epoch 6: loss did not improve from 0.24339
14/400 [>.............................] - ETA: 7:19 - loss: 0.6027 - dice_coefficient: 0.6418
Epoch 6: loss did not improve from 0.24339
15/400 [>.............................] - ETA: 7:35 - loss: 0.5770 - dice_coefficient: 0.6576
Epoch 6: loss did not improve from 0.24339
16/400 [>.............................] - ETA: 7:46 - loss: 0.6379 - dice_coefficient: 0.6320
Epoch 6: loss did not improve from 0.24339
17/400 [>.............................] - ETA: 7:38 - loss: 0.6146 - dice_coefficient: 0.6443
Epoch 6: loss did not improve from 0.24339
18/400 [>.............................] - ETA: 7:33 - loss: 0.6083 - dice_coefficient: 0.6443
Epoch 6: loss did not improve from 0.24339
19/400 [>.............................] - ETA: 7:28 - loss: 0.6209 - dice_coefficient: 0.6344
Epoch 6: loss did not improve from 0.24339
20/400 [>.............................] - ETA: 7:22 - loss: 0.6047 - dice_coefficient: 0.6424
Epoch 6: loss did not improve from 0.24339
21/400 [>.............................] - ETA: 7:17 - loss: 0.5940 - dice_coefficient: 0.6470
Epoch 6: loss did not improve from 0.24339
22/400 [>.............................] - ETA: 7:14 - loss: 0.5842 - dice_coefficient: 0.6525
Epoch 6: loss did not improve from 0.24339
23/400 [>.............................] - ETA: 7:09 - loss: 0.5682 - dice_coefficient: 0.6613
Epoch 6: loss did not improve from 0.24339
24/400 [>.............................] - ETA: 7:06 - loss: 0.5567 - dice_coefficient: 0.6674
Epoch 6: loss did not improve from 0.24339
25/400 [>.............................] - ETA: 7:02 - loss: 0.5465 - dice_coefficient: 0.6723
Epoch 6: loss did not improve from 0.24339
26/400 [>.............................] - ETA: 7:01 - loss: 0.5449 - dice_coefficient: 0.6775
Epoch 6: loss did not improve from 0.24339
27/400 [=>............................] - ETA: 7:07 - loss: 0.5616 - dice_coefficient: 0.6679
Epoch 6: loss did not improve from 0.24339
28/400 [=>............................] - ETA: 7:14 - loss: 0.5714 - dice_coefficient: 0.6605
Epoch 6: loss did not improve from 0.24339
29/400 [=>............................] - ETA: 7:12 - loss: 0.5654 - dice_coefficient: 0.6633
Epoch 6: loss did not improve from 0.24339
30/400 [=>............................] - ETA: 7:09 - loss: 0.5540 - dice_coefficient: 0.6700
Epoch 6: loss did not improve from 0.24339
31/400 [=>............................] - ETA: 7:06 - loss: 0.5464 - dice_coefficient: 0.6738
Epoch 6: loss did not improve from 0.24339
32/400 [=>............................] - ETA: 7:02 - loss: 0.5488 - dice_coefficient: 0.6706
Epoch 6: loss did not improve from 0.24339
33/400 [=>............................] - ETA: 7:00 - loss: 0.5420 - dice_coefficient: 0.6742
Epoch 6: loss did not improve from 0.24339
34/400 [=>............................] - ETA: 6:57 - loss: 0.5421 - dice_coefficient: 0.6784
Epoch 6: loss did not improve from 0.24339
35/400 [=>............................] - ETA: 6:54 - loss: 0.5374 - dice_coefficient: 0.6824
Epoch 6: loss did not improve from 0.24339
36/400 [=>............................] - ETA: 6:51 - loss: 0.5365 - dice_coefficient: 0.6820
Epoch 6: loss did not improve from 0.24339
37/400 [=>............................] - ETA: 6:49 - loss: 0.5629 - dice_coefficient: 0.6701
Epoch 6: loss did not improve from 0.24339
38/400 [=>............................] - ETA: 6:47 - loss: 0.5553 - dice_coefficient: 0.6741
Epoch 6: loss did not improve from 0.24339
39/400 [=>............................] - ETA: 6:50 - loss: 0.5525 - dice_coefficient: 0.6743
Epoch 6: loss did not improve from 0.24339
40/400 [==>...........................] - ETA: 6:55 - loss: 0.5718 - dice_coefficient: 0.6653
Epoch 6: loss did not improve from 0.24339
41/400 [==>...........................] - ETA: 6:53 - loss: 0.5729 - dice_coefficient: 0.6632
Epoch 6: loss did not improve from 0.24339
42/400 [==>...........................] - ETA: 6:50 - loss: 0.5670 - dice_coefficient: 0.6660
Epoch 6: loss did not improve from 0.24339
43/400 [==>...........................] - ETA: 6:47 - loss: 0.5591 - dice_coefficient: 0.6710
Epoch 6: loss did not improve from 0.24339
44/400 [==>...........................] - ETA: 6:44 - loss: 0.5582 - dice_coefficient: 0.6710
Epoch 6: loss did not improve from 0.24339
45/400 [==>...........................] - ETA: 6:42 - loss: 0.5520 - dice_coefficient: 0.6743
Epoch 6: loss did not improve from 0.24339
46/400 [==>...........................] - ETA: 6:40 - loss: 0.5480 - dice_coefficient: 0.6761
Epoch 6: loss did not improve from 0.24339
47/400 [==>...........................] - ETA: 6:38 - loss: 0.5455 - dice_coefficient: 0.6767
Epoch 6: loss did not improve from 0.24339
48/400 [==>...........................] - ETA: 6:36 - loss: 0.5409 - dice_coefficient: 0.6787
Epoch 6: loss did not improve from 0.24339
49/400 [==>...........................] - ETA: 6:34 - loss: 0.5605 - dice_coefficient: 0.6701
Epoch 6: loss did not improve from 0.24339
50/400 [==>...........................] - ETA: 6:32 - loss: 0.5540 - dice_coefficient: 0.6743
Epoch 6: loss did not improve from 0.24339
51/400 [==>...........................] - ETA: 6:32 - loss: 0.5521 - dice_coefficient: 0.6744
Epoch 6: loss did not improve from 0.24339
52/400 [==>...........................] - ETA: 6:36 - loss: 0.5539 - dice_coefficient: 0.6723
Epoch 6: loss did not improve from 0.24339
53/400 [==>...........................] - ETA: 6:36 - loss: 0.5540 - dice_coefficient: 0.6711
Epoch 6: loss did not improve from 0.24339
54/400 [===>..........................] - ETA: 6:34 - loss: 0.5578 - dice_coefficient: 0.6680
Epoch 6: loss did not improve from 0.24339
55/400 [===>..........................] - ETA: 6:31 - loss: 0.5634 - dice_coefficient: 0.6639
Epoch 6: loss did not improve from 0.24339
56/400 [===>..........................] - ETA: 6:29 - loss: 0.5618 - dice_coefficient: 0.6645
Epoch 6: loss did not improve from 0.24339
57/400 [===>..........................] - ETA: 6:27 - loss: 0.5607 - dice_coefficient: 0.6641
Epoch 6: loss did not improve from 0.24339
58/400 [===>..........................] - ETA: 6:26 - loss: 0.5563 - dice_coefficient: 0.6662
Epoch 6: loss did not improve from 0.24339
59/400 [===>..........................] - ETA: 6:24 - loss: 0.5523 - dice_coefficient: 0.6679
Epoch 6: loss did not improve from 0.24339
60/400 [===>..........................] - ETA: 6:22 - loss: 0.5504 - dice_coefficient: 0.6683
Epoch 6: loss did not improve from 0.24339
61/400 [===>..........................] - ETA: 6:20 - loss: 0.5490 - dice_coefficient: 0.6688
Epoch 6: loss did not improve from 0.24339
62/400 [===>..........................] - ETA: 6:18 - loss: 0.5454 - dice_coefficient: 0.6716
Epoch 6: loss did not improve from 0.24339
63/400 [===>..........................] - ETA: 6:16 - loss: 0.5420 - dice_coefficient: 0.6735
Epoch 6: loss did not improve from 0.24339
64/400 [===>..........................] - ETA: 6:21 - loss: 0.5371 - dice_coefficient: 0.6762
Epoch 6: loss did not improve from 0.24339
65/400 [===>..........................] - ETA: 6:22 - loss: 0.5396 - dice_coefficient: 0.6741
Epoch 6: loss did not improve from 0.24339
66/400 [===>..........................] - ETA: 6:20 - loss: 0.5377 - dice_coefficient: 0.6748
Epoch 6: loss did not improve from 0.24339
67/400 [====>.........................] - ETA: 6:19 - loss: 0.5413 - dice_coefficient: 0.6720
Epoch 6: loss did not improve from 0.24339
68/400 [====>.........................] - ETA: 6:17 - loss: 0.5360 - dice_coefficient: 0.6750
Epoch 6: loss did not improve from 0.24339
69/400 [====>.........................] - ETA: 6:15 - loss: 0.5547 - dice_coefficient: 0.6678
Epoch 6: loss did not improve from 0.24339
70/400 [====>.........................] - ETA: 6:12 - loss: 0.5510 - dice_coefficient: 0.6697
Epoch 6: loss did not improve from 0.24339
71/400 [====>.........................] - ETA: 6:10 - loss: 0.5471 - dice_coefficient: 0.6720
Epoch 6: loss did not improve from 0.24339
72/400 [====>.........................] - ETA: 6:08 - loss: 0.5436 - dice_coefficient: 0.6742
Epoch 6: loss did not improve from 0.24339
73/400 [====>.........................] - ETA: 6:06 - loss: 0.5400 - dice_coefficient: 0.6759
Epoch 6: loss did not improve from 0.24339
74/400 [====>.........................] - ETA: 6:04 - loss: 0.5426 - dice_coefficient: 0.6736
Epoch 6: loss did not improve from 0.24339
75/400 [====>.........................] - ETA: 6:03 - loss: 0.5399 - dice_coefficient: 0.6754
Epoch 6: loss did not improve from 0.24339
76/400 [====>.........................] - ETA: 6:04 - loss: 0.5423 - dice_coefficient: 0.6733
Epoch 6: loss did not improve from 0.24339
77/400 [====>.........................] - ETA: 6:06 - loss: 0.5383 - dice_coefficient: 0.6757
Epoch 6: loss did not improve from 0.24339
78/400 [====>.........................] - ETA: 6:04 - loss: 0.5480 - dice_coefficient: 0.6709
Epoch 6: loss did not improve from 0.24339
79/400 [====>.........................] - ETA: 6:02 - loss: 0.5508 - dice_coefficient: 0.6688
Epoch 6: loss did not improve from 0.24339
80/400 [=====>........................] - ETA: 6:00 - loss: 0.5471 - dice_coefficient: 0.6713
Epoch 6: loss did not improve from 0.24339
81/400 [=====>........................] - ETA: 5:59 - loss: 0.5503 - dice_coefficient: 0.6689
Epoch 6: loss did not improve from 0.24339
82/400 [=====>........................] - ETA: 5:57 - loss: 0.5466 - dice_coefficient: 0.6711
Epoch 6: loss did not improve from 0.24339
83/400 [=====>........................] - ETA: 5:56 - loss: 0.5423 - dice_coefficient: 0.6737
Epoch 6: loss did not improve from 0.24339
84/400 [=====>........................] - ETA: 5:55 - loss: 0.5383 - dice_coefficient: 0.6761
Epoch 6: loss did not improve from 0.24339
85/400 [=====>........................] - ETA: 5:54 - loss: 0.5343 - dice_coefficient: 0.6784
Epoch 6: loss did not improve from 0.24339
86/400 [=====>........................] - ETA: 5:52 - loss: 0.5357 - dice_coefficient: 0.6773
Epoch 6: loss did not improve from 0.24339
87/400 [=====>........................] - ETA: 5:51 - loss: 0.5321 - dice_coefficient: 0.6792
Epoch 6: loss did not improve from 0.24339
88/400 [=====>........................] - ETA: 5:53 - loss: 0.5310 - dice_coefficient: 0.6794
Epoch 6: loss did not improve from 0.24339
89/400 [=====>........................] - ETA: 5:53 - loss: 0.5272 - dice_coefficient: 0.6815
Epoch 6: loss did not improve from 0.24339
90/400 [=====>........................] - ETA: 5:52 - loss: 0.5246 - dice_coefficient: 0.6829
Epoch 6: loss did not improve from 0.24339
91/400 [=====>........................] - ETA: 5:51 - loss: 0.5221 - dice_coefficient: 0.6848
Epoch 6: loss did not improve from 0.24339
92/400 [=====>........................] - ETA: 5:49 - loss: 0.5203 - dice_coefficient: 0.6864
Epoch 6: loss did not improve from 0.24339
93/400 [=====>........................] - ETA: 5:48 - loss: 0.5175 - dice_coefficient: 0.6879
Epoch 6: loss did not improve from 0.24339
94/400 [======>.......................] - ETA: 5:46 - loss: 0.5147 - dice_coefficient: 0.6897
Epoch 6: loss did not improve from 0.24339
95/400 [======>.......................] - ETA: 5:44 - loss: 0.5158 - dice_coefficient: 0.6890
Epoch 6: loss did not improve from 0.24339
96/400 [======>.......................] - ETA: 5:43 - loss: 0.5138 - dice_coefficient: 0.6898
Epoch 6: loss did not improve from 0.24339
97/400 [======>.......................] - ETA: 5:41 - loss: 0.5115 - dice_coefficient: 0.6911
Epoch 6: loss did not improve from 0.24339
98/400 [======>.......................] - ETA: 5:39 - loss: 0.5126 - dice_coefficient: 0.6899
Epoch 6: loss did not improve from 0.24339
99/400 [======>.......................] - ETA: 5:39 - loss: 0.5109 - dice_coefficient: 0.6905
Epoch 6: loss did not improve from 0.24339
100/400 [======>.......................] - ETA: 5:40 - loss: 0.5083 - dice_coefficient: 0.6921
Epoch 6: loss did not improve from 0.24339
101/400 [======>.......................] - ETA: 5:40 - loss: 0.5088 - dice_coefficient: 0.6913
Epoch 6: loss did not improve from 0.24339
102/400 [======>.......................] - ETA: 5:40 - loss: 0.5105 - dice_coefficient: 0.6898
Epoch 6: loss did not improve from 0.24339
103/400 [======>.......................] - ETA: 5:38 - loss: 0.5079 - dice_coefficient: 0.6915
Epoch 6: loss did not improve from 0.24339
104/400 [======>.......................] - ETA: 5:37 - loss: 0.5055 - dice_coefficient: 0.6928
Epoch 6: loss did not improve from 0.24339
105/400 [======>.......................] - ETA: 5:35 - loss: 0.5130 - dice_coefficient: 0.6890
Epoch 6: loss did not improve from 0.24339
106/400 [======>.......................] - ETA: 5:34 - loss: 0.5140 - dice_coefficient: 0.6879
Epoch 6: loss did not improve from 0.24339
107/400 [=======>......................] - ETA: 5:33 - loss: 0.5142 - dice_coefficient: 0.6872
Epoch 6: loss did not improve from 0.24339
108/400 [=======>......................] - ETA: 5:32 - loss: 0.5168 - dice_coefficient: 0.6853
Epoch 6: loss did not improve from 0.24339
109/400 [=======>......................] - ETA: 5:30 - loss: 0.5183 - dice_coefficient: 0.6840
Epoch 6: loss did not improve from 0.24339
110/400 [=======>......................] - ETA: 5:29 - loss: 0.5162 - dice_coefficient: 0.6850
Epoch 6: loss did not improve from 0.24339
111/400 [=======>......................] - ETA: 5:30 - loss: 0.5138 - dice_coefficient: 0.6864
Epoch 6: loss did not improve from 0.24339
112/400 [=======>......................] - ETA: 5:30 - loss: 0.5114 - dice_coefficient: 0.6881
Epoch 6: loss did not improve from 0.24339
113/400 [=======>......................] - ETA: 5:28 - loss: 0.5140 - dice_coefficient: 0.6863
Epoch 6: loss did not improve from 0.24339
114/400 [=======>......................] - ETA: 5:27 - loss: 0.5170 - dice_coefficient: 0.6842
Epoch 6: loss did not improve from 0.24339
115/400 [=======>......................] - ETA: 5:26 - loss: 0.5144 - dice_coefficient: 0.6859
Epoch 6: loss did not improve from 0.24339
116/400 [=======>......................] - ETA: 5:24 - loss: 0.5130 - dice_coefficient: 0.6866
Epoch 6: loss did not improve from 0.24339
117/400 [=======>......................] - ETA: 5:22 - loss: 0.5129 - dice_coefficient: 0.6863
Epoch 6: loss did not improve from 0.24339
118/400 [=======>......................] - ETA: 5:21 - loss: 0.5174 - dice_coefficient: 0.6836
Epoch 6: loss did not improve from 0.24339
119/400 [=======>......................] - ETA: 5:19 - loss: 0.5160 - dice_coefficient: 0.6843
Epoch 6: loss did not improve from 0.24339
120/400 [========>.....................] - ETA: 5:18 - loss: 0.5140 - dice_coefficient: 0.6859
Epoch 6: loss did not improve from 0.24339
121/400 [========>.....................] - ETA: 5:16 - loss: 0.5165 - dice_coefficient: 0.6842
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
309/400 [======================>.......] - ETA: 1:43 - loss: 0.5261 - dice_coefficient: 0.7068
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
313/400 [======================>.......] - ETA: 1:38 - loss: 0.5230 - dice_coefficient: 0.7081
Epoch 6: loss did not improve from 0.24339
314/400 [======================>.......] - ETA: 1:37 - loss: 0.5231 - dice_coefficient: 0.7078
Epoch 6: loss did not improve from 0.24339
315/400 [======================>.......] - ETA: 1:36 - loss: 0.5220 - dice_coefficient: 0.7084
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
317/400 [======================>.......] - ETA: 1:34 - loss: 0.5205 - dice_coefficient: 0.7092
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
334/400 [========================>.....] - ETA: 1:14 - loss: 0.5129 - dice_coefficient: 0.7125
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
336/400 [========================>.....] - ETA: 1:12 - loss: 0.5125 - dice_coefficient: 0.7126
Epoch 6: loss did not improve from 0.24339
337/400 [========================>.....] - ETA: 1:11 - loss: 0.5121 - dice_coefficient: 0.7126
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
339/400 [========================>.....] - ETA: 1:08 - loss: 0.5108 - dice_coefficient: 0.7132
Epoch 6: loss did not improve from 0.24339
340/400 [========================>.....] - ETA: 1:07 - loss: 0.5108 - dice_coefficient: 0.7129
Epoch 6: loss did not improve from 0.24339
341/400 [========================>.....] - ETA: 1:06 - loss: 0.5102 - dice_coefficient: 0.7132
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
343/400 [========================>.....] - ETA: 1:04 - loss: 0.5100 - dice_coefficient: 0.7130
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
357/400 [=========================>....] - ETA: 49s - loss: 0.5123 - dice_coefficient: 0.7106
Epoch 6: loss did not improve from 0.24339
358/400 [=========================>....] - ETA: 48s - loss: 0.5114 - dice_coefficient: 0.7111
Epoch 6: loss did not improve from 0.24339
359/400 [=========================>....] - ETA: 47s - loss: 0.5113 - dice_coefficient: 0.7110
Epoch 6: loss did not improve from 0.24339
360/400 [==========================>...] - ETA: 46s - loss: 0.5108 - dice_coefficient: 0.7112
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
362/400 [==========================>...] - ETA: 43s - loss: 0.5098 - dice_coefficient: 0.7118
Epoch 6: loss did not improve from 0.24339
363/400 [==========================>...] - ETA: 42s - loss: 0.5126 - dice_coefficient: 0.7105
Epoch 6: loss did not improve from 0.24339
364/400 [==========================>...] - ETA: 41s - loss: 0.5119 - dice_coefficient: 0.7109
Epoch 6: loss did not improve from 0.24339
365/400 [==========================>...] - ETA: 40s - loss: 0.5113 - dice_coefficient: 0.7111
Epoch 6: loss did not improve from 0.24339
366/400 [==========================>...] - ETA: 39s - loss: 0.5117 - dice_coefficient: 0.7107
Epoch 6: loss did not improve from 0.24339
367/400 [==========================>...] - ETA: 37s - loss: 0.5115 - dice_coefficient: 0.7111
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
370/400 [==========================>...] - ETA: 34s - loss: 0.5158 - dice_coefficient: 0.7089
Epoch 6: loss did not improve from 0.24339
371/400 [==========================>...] - ETA: 33s - loss: 0.5152 - dice_coefficient: 0.7093
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
374/400 [===========================>..] - ETA: 29s - loss: 0.5162 - dice_coefficient: 0.7087
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
376/400 [===========================>..] - ETA: 27s - loss: 0.5168 - dice_coefficient: 0.7079
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
381/400 [===========================>..] - ETA: 21s - loss: 0.5146 - dice_coefficient: 0.7087
Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
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Epoch 6: loss did not improve from 0.24339
384/400 [===========================>..] - ETA: 18s - loss: 0.5127 - dice_coefficient: 0.7097
Epoch 6: loss did not improve from 0.24339
385/400 [===========================>..] - ETA: 17s - loss: 0.5121 - dice_coefficient: 0.7099
Epoch 6: loss did not improve from 0.24339
386/400 [===========================>..] - ETA: 16s - loss: 0.5117 - dice_coefficient: 0.7101
Epoch 6: loss did not improve from 0.24339
387/400 [============================>.] - ETA: 14s - loss: 0.5111 - dice_coefficient: 0.7105
Epoch 6: loss did not improve from 0.24339
388/400 [============================>.] - ETA: 13s - loss: 0.5105 - dice_coefficient: 0.7106
Epoch 6: loss did not improve from 0.24339
389/400 [============================>.] - ETA: 12s - loss: 0.5104 - dice_coefficient: 0.7105
Epoch 6: loss did not improve from 0.24339
390/400 [============================>.] - ETA: 11s - loss: 0.5102 - dice_coefficient: 0.7105
Epoch 6: loss did not improve from 0.24339
391/400 [============================>.] - ETA: 10s - loss: 0.5096 - dice_coefficient: 0.7107
Epoch 6: loss did not improve from 0.24339
392/400 [============================>.] - ETA: 9s - loss: 0.5088 - dice_coefficient: 0.7112
Epoch 6: loss did not improve from 0.24339
393/400 [============================>.] - ETA: 8s - loss: 0.5093 - dice_coefficient: 0.7107
Epoch 6: loss did not improve from 0.24339
394/400 [============================>.] - ETA: 6s - loss: 0.5090 - dice_coefficient: 0.7108
Epoch 6: loss did not improve from 0.24339
395/400 [============================>.] - ETA: 5s - loss: 0.5088 - dice_coefficient: 0.7108
Epoch 6: loss did not improve from 0.24339
396/400 [============================>.] - ETA: 4s - loss: 0.5085 - dice_coefficient: 0.7108
Epoch 6: loss did not improve from 0.24339
397/400 [============================>.] - ETA: 3s - loss: 0.5089 - dice_coefficient: 0.7104
Epoch 6: loss did not improve from 0.24339
398/400 [============================>.] - ETA: 2s - loss: 0.5082 - dice_coefficient: 0.7108
Epoch 6: loss did not improve from 0.24339
399/400 [============================>.] - ETA: 1s - loss: 0.5081 - dice_coefficient: 0.7111
Epoch 6: loss did not improve from 0.24339
400/400 [==============================] - 463s 1s/step - loss: 0.5097 - dice_coefficient: 0.7102 - val_loss: 1.0590 - val_dice_coefficient: 0.4762 - lr: 1.0000e-04
Epoch 7/10
Epoch 7: loss did not improve from 0.24339
1/400 [..............................] - ETA: 6:29 - loss: 0.4021 - dice_coefficient: 0.7100
Epoch 7: loss did not improve from 0.24339
2/400 [..............................] - ETA: 6:03 - loss: 0.3982 - dice_coefficient: 0.7389
Epoch 7: loss did not improve from 0.24339
3/400 [..............................] - ETA: 6:04 - loss: 0.4002 - dice_coefficient: 0.7248
Epoch 7: loss did not improve from 0.24339
4/400 [..............................] - ETA: 6:17 - loss: 0.3722 - dice_coefficient: 0.7498
Epoch 7: loss did not improve from 0.24339
5/400 [..............................] - ETA: 6:26 - loss: 0.3499 - dice_coefficient: 0.7735
Epoch 7: loss did not improve from 0.24339
6/400 [..............................] - ETA: 6:38 - loss: 0.3332 - dice_coefficient: 0.7912
Epoch 7: loss did not improve from 0.24339
7/400 [..............................] - ETA: 7:30 - loss: 0.3363 - dice_coefficient: 0.7850
Epoch 7: loss did not improve from 0.24339
8/400 [..............................] - ETA: 7:49 - loss: 0.3603 - dice_coefficient: 0.7658
Epoch 7: loss did not improve from 0.24339
9/400 [..............................] - ETA: 7:35 - loss: 0.3550 - dice_coefficient: 0.7681
Epoch 7: loss did not improve from 0.24339
10/400 [..............................] - ETA: 7:31 - loss: 0.3895 - dice_coefficient: 0.7433
Epoch 7: loss did not improve from 0.24339
11/400 [..............................] - ETA: 7:31 - loss: 0.4182 - dice_coefficient: 0.7232
Epoch 7: loss did not improve from 0.24339
12/400 [..............................] - ETA: 7:21 - loss: 0.4379 - dice_coefficient: 0.7085
Epoch 7: loss did not improve from 0.24339
13/400 [..............................] - ETA: 7:13 - loss: 0.4290 - dice_coefficient: 0.7120
Epoch 7: loss did not improve from 0.24339
14/400 [>.............................] - ETA: 7:07 - loss: 0.4140 - dice_coefficient: 0.7216
Epoch 7: loss did not improve from 0.24339
15/400 [>.............................] - ETA: 7:02 - loss: 0.4220 - dice_coefficient: 0.7148
Epoch 7: loss did not improve from 0.24339
16/400 [>.............................] - ETA: 6:58 - loss: 0.4078 - dice_coefficient: 0.7252
Epoch 7: loss did not improve from 0.24339
17/400 [>.............................] - ETA: 6:53 - loss: 0.4311 - dice_coefficient: 0.7119
Epoch 7: loss did not improve from 0.24339
18/400 [>.............................] - ETA: 6:49 - loss: 0.4170 - dice_coefficient: 0.7217
Epoch 7: loss did not improve from 0.24339
19/400 [>.............................] - ETA: 6:59 - loss: 0.4041 - dice_coefficient: 0.7300
Epoch 7: loss did not improve from 0.24339
20/400 [>.............................] - ETA: 7:10 - loss: 0.3995 - dice_coefficient: 0.7319
Epoch 7: loss did not improve from 0.24339
21/400 [>.............................] - ETA: 7:07 - loss: 0.3993 - dice_coefficient: 0.7307
Epoch 7: loss did not improve from 0.24339
22/400 [>.............................] - ETA: 7:02 - loss: 0.3951 - dice_coefficient: 0.7327
Epoch 7: loss did not improve from 0.24339
23/400 [>.............................] - ETA: 7:00 - loss: 0.3892 - dice_coefficient: 0.7360
Epoch 7: loss did not improve from 0.24339
24/400 [>.............................] - ETA: 6:59 - loss: 0.3872 - dice_coefficient: 0.7370
Epoch 7: loss did not improve from 0.24339
25/400 [>.............................] - ETA: 6:55 - loss: 0.3963 - dice_coefficient: 0.7355
Epoch 7: loss did not improve from 0.24339
26/400 [>.............................] - ETA: 6:52 - loss: 0.3971 - dice_coefficient: 0.7342
Epoch 7: loss did not improve from 0.24339
27/400 [=>............................] - ETA: 6:48 - loss: 0.3967 - dice_coefficient: 0.7334
Epoch 7: loss did not improve from 0.24339
28/400 [=>............................] - ETA: 6:45 - loss: 0.3945 - dice_coefficient: 0.7352
Epoch 7: loss did not improve from 0.24339
29/400 [=>............................] - ETA: 6:41 - loss: 0.3920 - dice_coefficient: 0.7360
Epoch 7: loss did not improve from 0.24339
30/400 [=>............................] - ETA: 6:38 - loss: 0.3932 - dice_coefficient: 0.7348
Epoch 7: loss did not improve from 0.24339
31/400 [=>............................] - ETA: 6:41 - loss: 0.3901 - dice_coefficient: 0.7373
Epoch 7: loss did not improve from 0.24339
32/400 [=>............................] - ETA: 6:49 - loss: 0.3882 - dice_coefficient: 0.7400
Epoch 7: loss did not improve from 0.24339
33/400 [=>............................] - ETA: 6:52 - loss: 0.3805 - dice_coefficient: 0.7453
Epoch 7: loss did not improve from 0.24339
34/400 [=>............................] - ETA: 6:49 - loss: 0.3772 - dice_coefficient: 0.7488
Epoch 7: loss did not improve from 0.24339
35/400 [=>............................] - ETA: 6:48 - loss: 0.3796 - dice_coefficient: 0.7469
Epoch 7: loss did not improve from 0.24339
36/400 [=>............................] - ETA: 6:46 - loss: 0.3986 - dice_coefficient: 0.7370
Epoch 7: loss did not improve from 0.24339
37/400 [=>............................] - ETA: 6:45 - loss: 0.3941 - dice_coefficient: 0.7405
Epoch 7: loss did not improve from 0.24339
38/400 [=>............................] - ETA: 6:43 - loss: 0.4033 - dice_coefficient: 0.7341
Epoch 7: loss did not improve from 0.24339
39/400 [=>............................] - ETA: 6:41 - loss: 0.3968 - dice_coefficient: 0.7383
Epoch 7: loss did not improve from 0.24339
40/400 [==>...........................] - ETA: 6:40 - loss: 0.3955 - dice_coefficient: 0.7383
Epoch 7: loss did not improve from 0.24339
41/400 [==>...........................] - ETA: 6:37 - loss: 0.3909 - dice_coefficient: 0.7414
Epoch 7: loss did not improve from 0.24339
42/400 [==>...........................] - ETA: 6:34 - loss: 0.3871 - dice_coefficient: 0.7438
Epoch 7: loss did not improve from 0.24339
43/400 [==>...........................] - ETA: 6:38 - loss: 0.3862 - dice_coefficient: 0.7439
Epoch 7: loss did not improve from 0.24339
44/400 [==>...........................] - ETA: 6:43 - loss: 0.4054 - dice_coefficient: 0.7342
Epoch 7: loss did not improve from 0.24339
45/400 [==>...........................] - ETA: 6:42 - loss: 0.4015 - dice_coefficient: 0.7363
Epoch 7: loss did not improve from 0.24339
46/400 [==>...........................] - ETA: 6:40 - loss: 0.4031 - dice_coefficient: 0.7345
Epoch 7: loss did not improve from 0.24339
47/400 [==>...........................] - ETA: 6:38 - loss: 0.4033 - dice_coefficient: 0.7345
Epoch 7: loss did not improve from 0.24339
48/400 [==>...........................] - ETA: 6:37 - loss: 0.3985 - dice_coefficient: 0.7378
Epoch 7: loss did not improve from 0.24339
49/400 [==>...........................] - ETA: 6:34 - loss: 0.3990 - dice_coefficient: 0.7367
Epoch 7: loss did not improve from 0.24339
50/400 [==>...........................] - ETA: 6:32 - loss: 0.3941 - dice_coefficient: 0.7398
Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
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Epoch 7: loss did not improve from 0.24339
378/400 [===========================>..] - ETA: 24s - loss: 0.4256 - dice_coefficient: 0.7528
Epoch 7: loss did not improve from 0.24339
379/400 [===========================>..] - ETA: 23s - loss: 0.4260 - dice_coefficient: 0.7523
Epoch 7: loss did not improve from 0.24339
380/400 [===========================>..] - ETA: 22s - loss: 0.4262 - dice_coefficient: 0.7525
Epoch 7: loss did not improve from 0.24339
381/400 [===========================>..] - ETA: 21s - loss: 0.4254 - dice_coefficient: 0.7530
Epoch 7: loss did not improve from 0.24339
382/400 [===========================>..] - ETA: 20s - loss: 0.4250 - dice_coefficient: 0.7533
Epoch 7: loss did not improve from 0.24339
383/400 [===========================>..] - ETA: 19s - loss: 0.4244 - dice_coefficient: 0.7537
Epoch 7: loss did not improve from 0.24339
384/400 [===========================>..] - ETA: 18s - loss: 0.4236 - dice_coefficient: 0.7541
Epoch 7: loss did not improve from 0.24339
385/400 [===========================>..] - ETA: 16s - loss: 0.4253 - dice_coefficient: 0.7531
Epoch 7: loss did not improve from 0.24339
386/400 [===========================>..] - ETA: 15s - loss: 0.4249 - dice_coefficient: 0.7533
Epoch 7: loss did not improve from 0.24339
387/400 [============================>.] - ETA: 14s - loss: 0.4241 - dice_coefficient: 0.7537
Epoch 7: loss did not improve from 0.24339
388/400 [============================>.] - ETA: 13s - loss: 0.4241 - dice_coefficient: 0.7536
Epoch 7: loss did not improve from 0.24339
389/400 [============================>.] - ETA: 12s - loss: 0.4236 - dice_coefficient: 0.7537
Epoch 7: loss did not improve from 0.24339
390/400 [============================>.] - ETA: 11s - loss: 0.4231 - dice_coefficient: 0.7539
Epoch 7: loss did not improve from 0.24339
391/400 [============================>.] - ETA: 10s - loss: 0.4229 - dice_coefficient: 0.7540
Epoch 7: loss did not improve from 0.24339
392/400 [============================>.] - ETA: 9s - loss: 0.4233 - dice_coefficient: 0.7536
Epoch 7: loss did not improve from 0.24339
393/400 [============================>.] - ETA: 7s - loss: 0.4227 - dice_coefficient: 0.7539
Epoch 7: loss did not improve from 0.24339
394/400 [============================>.] - ETA: 6s - loss: 0.4220 - dice_coefficient: 0.7543
Epoch 7: loss did not improve from 0.24339
395/400 [============================>.] - ETA: 5s - loss: 0.4213 - dice_coefficient: 0.7546
Epoch 7: loss did not improve from 0.24339
396/400 [============================>.] - ETA: 4s - loss: 0.4209 - dice_coefficient: 0.7548
Epoch 7: loss did not improve from 0.24339
397/400 [============================>.] - ETA: 3s - loss: 0.4202 - dice_coefficient: 0.7552
Epoch 7: loss did not improve from 0.24339
398/400 [============================>.] - ETA: 2s - loss: 0.4206 - dice_coefficient: 0.7548
Epoch 7: loss did not improve from 0.24339
399/400 [============================>.] - ETA: 1s - loss: 0.4199 - dice_coefficient: 0.7551
Epoch 7: loss did not improve from 0.24339
400/400 [==============================] - 455s 1s/step - loss: 0.4193 - dice_coefficient: 0.7555 - val_loss: 0.8481 - val_dice_coefficient: 0.5480 - lr: 1.0000e-04
Epoch 8/10
Epoch 8: loss did not improve from 0.24339
1/400 [..............................] - ETA: 11:25 - loss: 0.3840 - dice_coefficient: 0.7421
Epoch 8: loss did not improve from 0.24339
2/400 [..............................] - ETA: 11:49 - loss: 0.4614 - dice_coefficient: 0.6806
Epoch 8: loss did not improve from 0.24339
3/400 [..............................] - ETA: 9:03 - loss: 0.4245 - dice_coefficient: 0.6978
Epoch 8: loss did not improve from 0.24339
4/400 [..............................] - ETA: 8:09 - loss: 0.4238 - dice_coefficient: 0.6956
Epoch 8: loss did not improve from 0.24339
5/400 [..............................] - ETA: 7:42 - loss: 0.3875 - dice_coefficient: 0.7331
Epoch 8: loss did not improve from 0.24339
6/400 [..............................] - ETA: 7:52 - loss: 0.3539 - dice_coefficient: 0.7594
Epoch 8: loss did not improve from 0.24339
7/400 [..............................] - ETA: 7:41 - loss: 0.3261 - dice_coefficient: 0.7800
Epoch 8: loss did not improve from 0.24339
8/400 [..............................] - ETA: 7:28 - loss: 0.3158 - dice_coefficient: 0.7893
Epoch 8: loss did not improve from 0.24339
9/400 [..............................] - ETA: 7:15 - loss: 0.3245 - dice_coefficient: 0.7797
Epoch 8: loss did not improve from 0.24339
10/400 [..............................] - ETA: 7:09 - loss: 0.3096 - dice_coefficient: 0.7895
Epoch 8: loss did not improve from 0.24339
11/400 [..............................] - ETA: 7:05 - loss: 0.2964 - dice_coefficient: 0.7993
Epoch 8: loss did not improve from 0.24339
12/400 [..............................] - ETA: 7:03 - loss: 0.3046 - dice_coefficient: 0.7912
Epoch 8: loss did not improve from 0.24339
13/400 [..............................] - ETA: 7:21 - loss: 0.3026 - dice_coefficient: 0.7935
Epoch 8: loss did not improve from 0.24339
14/400 [>.............................] - ETA: 7:38 - loss: 0.3739 - dice_coefficient: 0.7573
Epoch 8: loss did not improve from 0.24339
15/400 [>.............................] - ETA: 7:30 - loss: 0.3684 - dice_coefficient: 0.7585
Epoch 8: loss did not improve from 0.24339
16/400 [>.............................] - ETA: 7:26 - loss: 0.3637 - dice_coefficient: 0.7610
Epoch 8: loss did not improve from 0.24339
17/400 [>.............................] - ETA: 7:22 - loss: 0.3530 - dice_coefficient: 0.7694
Epoch 8: loss did not improve from 0.24339
18/400 [>.............................] - ETA: 7:19 - loss: 0.3491 - dice_coefficient: 0.7747
Epoch 8: loss did not improve from 0.24339
19/400 [>.............................] - ETA: 7:15 - loss: 0.3393 - dice_coefficient: 0.7807
Epoch 8: loss did not improve from 0.24339
20/400 [>.............................] - ETA: 7:14 - loss: 0.3289 - dice_coefficient: 0.7879
Epoch 8: loss did not improve from 0.24339
21/400 [>.............................] - ETA: 7:10 - loss: 0.3200 - dice_coefficient: 0.7935
Epoch 8: loss did not improve from 0.24339
22/400 [>.............................] - ETA: 7:07 - loss: 0.3178 - dice_coefficient: 0.7938
Epoch 8: loss did not improve from 0.24339
23/400 [>.............................] - ETA: 7:03 - loss: 0.3184 - dice_coefficient: 0.7921
Epoch 8: loss did not improve from 0.24339
24/400 [>.............................] - ETA: 7:02 - loss: 0.3211 - dice_coefficient: 0.7885
Epoch 8: loss did not improve from 0.24339
25/400 [>.............................] - ETA: 7:09 - loss: 0.3147 - dice_coefficient: 0.7931
Epoch 8: loss did not improve from 0.24339
26/400 [>.............................] - ETA: 7:14 - loss: 0.3102 - dice_coefficient: 0.7963
Epoch 8: loss did not improve from 0.24339
27/400 [=>............................] - ETA: 7:10 - loss: 0.3141 - dice_coefficient: 0.7921
Epoch 8: loss did not improve from 0.24339
28/400 [=>............................] - ETA: 7:07 - loss: 0.3103 - dice_coefficient: 0.7944
Epoch 8: loss did not improve from 0.24339
29/400 [=>............................] - ETA: 7:03 - loss: 0.3134 - dice_coefficient: 0.7912
Epoch 8: loss did not improve from 0.24339
30/400 [=>............................] - ETA: 7:00 - loss: 0.3094 - dice_coefficient: 0.7940
Epoch 8: loss did not improve from 0.24339
31/400 [=>............................] - ETA: 6:58 - loss: 0.3079 - dice_coefficient: 0.7945
Epoch 8: loss did not improve from 0.24339
32/400 [=>............................] - ETA: 6:55 - loss: 0.3042 - dice_coefficient: 0.7979
Epoch 8: loss did not improve from 0.24339
33/400 [=>............................] - ETA: 6:51 - loss: 0.3022 - dice_coefficient: 0.8006
Epoch 8: loss did not improve from 0.24339
34/400 [=>............................] - ETA: 6:48 - loss: 0.2999 - dice_coefficient: 0.8021
Epoch 8: loss did not improve from 0.24339
35/400 [=>............................] - ETA: 6:46 - loss: 0.3010 - dice_coefficient: 0.8004
Epoch 8: loss did not improve from 0.24339
36/400 [=>............................] - ETA: 6:45 - loss: 0.2989 - dice_coefficient: 0.8019
Epoch 8: loss did not improve from 0.24339
37/400 [=>............................] - ETA: 6:49 - loss: 0.3154 - dice_coefficient: 0.7917
Epoch 8: loss did not improve from 0.24339
38/400 [=>............................] - ETA: 6:53 - loss: 0.3348 - dice_coefficient: 0.7805
Epoch 8: loss did not improve from 0.24339
39/400 [=>............................] - ETA: 6:52 - loss: 0.3310 - dice_coefficient: 0.7828
Epoch 8: loss did not improve from 0.24339
40/400 [==>...........................] - ETA: 6:50 - loss: 0.3339 - dice_coefficient: 0.7800
Epoch 8: loss did not improve from 0.24339
41/400 [==>...........................] - ETA: 6:47 - loss: 0.3305 - dice_coefficient: 0.7826
Epoch 8: loss did not improve from 0.24339
42/400 [==>...........................] - ETA: 6:44 - loss: 0.3303 - dice_coefficient: 0.7824
Epoch 8: loss did not improve from 0.24339
43/400 [==>...........................] - ETA: 6:43 - loss: 0.3277 - dice_coefficient: 0.7843
Epoch 8: loss did not improve from 0.24339
44/400 [==>...........................] - ETA: 6:40 - loss: 0.3260 - dice_coefficient: 0.7851
Epoch 8: loss did not improve from 0.24339
45/400 [==>...........................] - ETA: 6:38 - loss: 0.3245 - dice_coefficient: 0.7856
Epoch 8: loss did not improve from 0.24339
46/400 [==>...........................] - ETA: 6:35 - loss: 0.3270 - dice_coefficient: 0.7833
Epoch 8: loss did not improve from 0.24339
47/400 [==>...........................] - ETA: 6:33 - loss: 0.3256 - dice_coefficient: 0.7853
Epoch 8: loss did not improve from 0.24339
48/400 [==>...........................] - ETA: 6:31 - loss: 0.3402 - dice_coefficient: 0.7767
Epoch 8: loss did not improve from 0.24339
49/400 [==>...........................] - ETA: 6:33 - loss: 0.3388 - dice_coefficient: 0.7788
Epoch 8: loss did not improve from 0.24339
50/400 [==>...........................] - ETA: 6:37 - loss: 0.3366 - dice_coefficient: 0.7809
Epoch 8: loss did not improve from 0.24339
51/400 [==>...........................] - ETA: 6:37 - loss: 0.3332 - dice_coefficient: 0.7835
Epoch 8: loss did not improve from 0.24339
52/400 [==>...........................] - ETA: 6:35 - loss: 0.3309 - dice_coefficient: 0.7846
Epoch 8: loss did not improve from 0.24339
53/400 [==>...........................] - ETA: 6:33 - loss: 0.3277 - dice_coefficient: 0.7866
Epoch 8: loss did not improve from 0.24339
54/400 [===>..........................] - ETA: 6:31 - loss: 0.3264 - dice_coefficient: 0.7872
Epoch 8: loss did not improve from 0.24339
55/400 [===>..........................] - ETA: 6:29 - loss: 0.3230 - dice_coefficient: 0.7894
Epoch 8: loss did not improve from 0.24339
56/400 [===>..........................] - ETA: 6:27 - loss: 0.3292 - dice_coefficient: 0.7848
Epoch 8: loss did not improve from 0.24339
57/400 [===>..........................] - ETA: 6:26 - loss: 0.3273 - dice_coefficient: 0.7866
Epoch 8: loss did not improve from 0.24339
58/400 [===>..........................] - ETA: 6:24 - loss: 0.3304 - dice_coefficient: 0.7838
Epoch 8: loss did not improve from 0.24339
59/400 [===>..........................] - ETA: 6:22 - loss: 0.3318 - dice_coefficient: 0.7821
Epoch 8: loss did not improve from 0.24339
60/400 [===>..........................] - ETA: 6:21 - loss: 0.3329 - dice_coefficient: 0.7808
Epoch 8: loss did not improve from 0.24339
61/400 [===>..........................] - ETA: 6:23 - loss: 0.3320 - dice_coefficient: 0.7810
Epoch 8: loss did not improve from 0.24339
62/400 [===>..........................] - ETA: 6:26 - loss: 0.3290 - dice_coefficient: 0.7829
Epoch 8: loss did not improve from 0.24339
63/400 [===>..........................] - ETA: 6:23 - loss: 0.3414 - dice_coefficient: 0.7763
Epoch 8: loss did not improve from 0.24339
64/400 [===>..........................] - ETA: 6:21 - loss: 0.3414 - dice_coefficient: 0.7762
Epoch 8: loss did not improve from 0.24339
65/400 [===>..........................] - ETA: 6:19 - loss: 0.3436 - dice_coefficient: 0.7741
Epoch 8: loss did not improve from 0.24339
66/400 [===>..........................] - ETA: 6:17 - loss: 0.3436 - dice_coefficient: 0.7744
Epoch 8: loss did not improve from 0.24339
67/400 [====>.........................] - ETA: 6:15 - loss: 0.3410 - dice_coefficient: 0.7763
Epoch 8: loss did not improve from 0.24339
68/400 [====>.........................] - ETA: 6:14 - loss: 0.3384 - dice_coefficient: 0.7781
Epoch 8: loss did not improve from 0.24339
69/400 [====>.........................] - ETA: 6:12 - loss: 0.3362 - dice_coefficient: 0.7793
Epoch 8: loss did not improve from 0.24339
70/400 [====>.........................] - ETA: 6:10 - loss: 0.3353 - dice_coefficient: 0.7804
Epoch 8: loss did not improve from 0.24339
71/400 [====>.........................] - ETA: 6:08 - loss: 0.3350 - dice_coefficient: 0.7806
Epoch 8: loss did not improve from 0.24339
72/400 [====>.........................] - ETA: 6:06 - loss: 0.3331 - dice_coefficient: 0.7819
Epoch 8: loss did not improve from 0.24339
73/400 [====>.........................] - ETA: 6:06 - loss: 0.3331 - dice_coefficient: 0.7814
Epoch 8: loss did not improve from 0.24339
74/400 [====>.........................] - ETA: 6:09 - loss: 0.3303 - dice_coefficient: 0.7834
Epoch 8: loss did not improve from 0.24339
75/400 [====>.........................] - ETA: 6:13 - loss: 0.3343 - dice_coefficient: 0.7803
Epoch 8: loss did not improve from 0.24339
76/400 [====>.........................] - ETA: 6:16 - loss: 0.3325 - dice_coefficient: 0.7814
Epoch 8: loss did not improve from 0.24339
77/400 [====>.........................] - ETA: 6:18 - loss: 0.3311 - dice_coefficient: 0.7821
Epoch 8: loss did not improve from 0.24339
78/400 [====>.........................] - ETA: 6:22 - loss: 0.3285 - dice_coefficient: 0.7838
Epoch 8: loss did not improve from 0.24339
79/400 [====>.........................] - ETA: 6:24 - loss: 0.3269 - dice_coefficient: 0.7852
Epoch 8: loss did not improve from 0.24339
80/400 [=====>........................] - ETA: 6:25 - loss: 0.3261 - dice_coefficient: 0.7858
Epoch 8: loss did not improve from 0.24339
81/400 [=====>........................] - ETA: 6:26 - loss: 0.3286 - dice_coefficient: 0.7837
Epoch 8: loss did not improve from 0.24339
82/400 [=====>........................] - ETA: 6:26 - loss: 0.5229 - dice_coefficient: 0.7742
Epoch 8: loss did not improve from 0.24339
83/400 [=====>........................] - ETA: 6:24 - loss: 0.5226 - dice_coefficient: 0.7727
Epoch 8: loss did not improve from 0.24339
84/400 [=====>........................] - ETA: 6:22 - loss: 0.5281 - dice_coefficient: 0.7682
Epoch 8: loss did not improve from 0.24339
85/400 [=====>........................] - ETA: 6:20 - loss: 0.5239 - dice_coefficient: 0.7697
Epoch 8: loss did not improve from 0.24339
86/400 [=====>........................] - ETA: 6:18 - loss: 0.5193 - dice_coefficient: 0.7715
Epoch 8: loss did not improve from 0.24339
87/400 [=====>........................] - ETA: 6:16 - loss: 0.5200 - dice_coefficient: 0.7693
Epoch 8: loss did not improve from 0.24339
88/400 [=====>........................] - ETA: 6:14 - loss: 0.5161 - dice_coefficient: 0.7705
Epoch 8: loss did not improve from 0.24339
89/400 [=====>........................] - ETA: 6:12 - loss: 0.5131 - dice_coefficient: 0.7717
Epoch 8: loss did not improve from 0.24339
90/400 [=====>........................] - ETA: 6:11 - loss: 0.5089 - dice_coefficient: 0.7733
Epoch 8: loss did not improve from 0.24339
91/400 [=====>........................] - ETA: 6:09 - loss: 0.5101 - dice_coefficient: 0.7710
Epoch 8: loss did not improve from 0.24339
92/400 [=====>........................] - ETA: 6:07 - loss: 0.5101 - dice_coefficient: 0.7696
Epoch 8: loss did not improve from 0.24339
93/400 [=====>........................] - ETA: 6:08 - loss: 0.5090 - dice_coefficient: 0.7690
Epoch 8: loss did not improve from 0.24339
94/400 [======>.......................] - ETA: 6:08 - loss: 0.5096 - dice_coefficient: 0.7672
Epoch 8: loss did not improve from 0.24339
95/400 [======>.......................] - ETA: 6:07 - loss: 0.5077 - dice_coefficient: 0.7672
Epoch 8: loss did not improve from 0.24339
96/400 [======>.......................] - ETA: 6:05 - loss: 0.5062 - dice_coefficient: 0.7678
Epoch 8: loss did not improve from 0.24339
97/400 [======>.......................] - ETA: 6:03 - loss: 0.5036 - dice_coefficient: 0.7684
Epoch 8: loss did not improve from 0.24339
98/400 [======>.......................] - ETA: 6:02 - loss: 0.5053 - dice_coefficient: 0.7660
Epoch 8: loss did not improve from 0.24339
99/400 [======>.......................] - ETA: 6:00 - loss: 0.5018 - dice_coefficient: 0.7675
Epoch 8: loss did not improve from 0.24339
100/400 [======>.......................] - ETA: 5:58 - loss: 0.4988 - dice_coefficient: 0.7685
Epoch 8: loss did not improve from 0.24339
101/400 [======>.......................] - ETA: 5:56 - loss: 0.4958 - dice_coefficient: 0.7694
Epoch 8: loss did not improve from 0.24339
102/400 [======>.......................] - ETA: 5:54 - loss: 0.4948 - dice_coefficient: 0.7689
Epoch 8: loss did not improve from 0.24339
103/400 [======>.......................] - ETA: 5:52 - loss: 0.4928 - dice_coefficient: 0.7693
Epoch 8: loss did not improve from 0.24339
104/400 [======>.......................] - ETA: 5:51 - loss: 0.4900 - dice_coefficient: 0.7705
Epoch 8: loss did not improve from 0.24339
105/400 [======>.......................] - ETA: 5:51 - loss: 0.4868 - dice_coefficient: 0.7717
Epoch 8: loss did not improve from 0.24339
106/400 [======>.......................] - ETA: 5:51 - loss: 0.4840 - dice_coefficient: 0.7728
Epoch 8: loss did not improve from 0.24339
107/400 [=======>......................] - ETA: 5:50 - loss: 0.4822 - dice_coefficient: 0.7733
Epoch 8: loss did not improve from 0.24339
108/400 [=======>......................] - ETA: 5:48 - loss: 0.4804 - dice_coefficient: 0.7734
Epoch 8: loss did not improve from 0.24339
109/400 [=======>......................] - ETA: 5:46 - loss: 0.4786 - dice_coefficient: 0.7742
Epoch 8: loss did not improve from 0.24339
110/400 [=======>......................] - ETA: 5:44 - loss: 0.4778 - dice_coefficient: 0.7738
Epoch 8: loss did not improve from 0.24339
111/400 [=======>......................] - ETA: 5:43 - loss: 0.4766 - dice_coefficient: 0.7746
Epoch 8: loss did not improve from 0.24339
112/400 [=======>......................] - ETA: 5:41 - loss: 0.4747 - dice_coefficient: 0.7749
Epoch 8: loss did not improve from 0.24339
113/400 [=======>......................] - ETA: 5:40 - loss: 0.4728 - dice_coefficient: 0.7756
Epoch 8: loss did not improve from 0.24339
114/400 [=======>......................] - ETA: 5:38 - loss: 0.4697 - dice_coefficient: 0.7769
Epoch 8: loss did not improve from 0.24339
115/400 [=======>......................] - ETA: 5:36 - loss: 0.4671 - dice_coefficient: 0.7780
Epoch 8: loss did not improve from 0.24339
116/400 [=======>......................] - ETA: 5:35 - loss: 0.4657 - dice_coefficient: 0.7780
Epoch 8: loss did not improve from 0.24339
117/400 [=======>......................] - ETA: 5:35 - loss: 0.4638 - dice_coefficient: 0.7786
Epoch 8: loss did not improve from 0.24339
118/400 [=======>......................] - ETA: 5:35 - loss: 0.4622 - dice_coefficient: 0.7787
Epoch 8: loss did not improve from 0.24339
119/400 [=======>......................] - ETA: 5:34 - loss: 0.4664 - dice_coefficient: 0.7756
Epoch 8: loss did not improve from 0.24339
120/400 [========>.....................] - ETA: 5:32 - loss: 0.4688 - dice_coefficient: 0.7732
Epoch 8: loss did not improve from 0.24339
121/400 [========>.....................] - ETA: 5:31 - loss: 0.4696 - dice_coefficient: 0.7719
Epoch 8: loss did not improve from 0.24339
122/400 [========>.....................] - ETA: 5:29 - loss: 0.4668 - dice_coefficient: 0.7731
Epoch 8: loss did not improve from 0.24339
123/400 [========>.....................] - ETA: 5:28 - loss: 0.4653 - dice_coefficient: 0.7733
Epoch 8: loss did not improve from 0.24339
124/400 [========>.....................] - ETA: 5:26 - loss: 0.4643 - dice_coefficient: 0.7736
Epoch 8: loss did not improve from 0.24339
125/400 [========>.....................] - ETA: 5:24 - loss: 0.4625 - dice_coefficient: 0.7741
Epoch 8: loss did not improve from 0.24339
126/400 [========>.....................] - ETA: 5:23 - loss: 0.4604 - dice_coefficient: 0.7749
Epoch 8: loss did not improve from 0.24339
127/400 [========>.....................] - ETA: 5:21 - loss: 0.4583 - dice_coefficient: 0.7759
Epoch 8: loss did not improve from 0.24339
128/400 [========>.....................] - ETA: 5:20 - loss: 0.4559 - dice_coefficient: 0.7770
Epoch 8: loss did not improve from 0.24339
129/400 [========>.....................] - ETA: 5:20 - loss: 0.4558 - dice_coefficient: 0.7762
Epoch 8: loss did not improve from 0.24339
130/400 [========>.....................] - ETA: 5:21 - loss: 0.4547 - dice_coefficient: 0.7764
Epoch 8: loss did not improve from 0.24339
131/400 [========>.....................] - ETA: 5:19 - loss: 0.4533 - dice_coefficient: 0.7767
Epoch 8: loss did not improve from 0.24339
132/400 [========>.....................] - ETA: 5:18 - loss: 0.4564 - dice_coefficient: 0.7742
Epoch 8: loss did not improve from 0.24339
133/400 [========>.....................] - ETA: 5:16 - loss: 0.4553 - dice_coefficient: 0.7750
Epoch 8: loss did not improve from 0.24339
134/400 [=========>....................] - ETA: 5:15 - loss: 0.4535 - dice_coefficient: 0.7755
Epoch 8: loss did not improve from 0.24339
135/400 [=========>....................] - ETA: 5:13 - loss: 0.4517 - dice_coefficient: 0.7762
Epoch 8: loss did not improve from 0.24339
136/400 [=========>....................] - ETA: 5:12 - loss: 0.4509 - dice_coefficient: 0.7761
Epoch 8: loss did not improve from 0.24339
137/400 [=========>....................] - ETA: 5:10 - loss: 0.4496 - dice_coefficient: 0.7763
Epoch 8: loss did not improve from 0.24339
138/400 [=========>....................] - ETA: 5:09 - loss: 0.4476 - dice_coefficient: 0.7771
Epoch 8: loss did not improve from 0.24339
139/400 [=========>....................] - ETA: 5:07 - loss: 0.4458 - dice_coefficient: 0.7780
Epoch 8: loss did not improve from 0.24339
140/400 [=========>....................] - ETA: 5:06 - loss: 0.4455 - dice_coefficient: 0.7773
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Epoch 8: loss did not improve from 0.24339
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Epoch 8: loss did not improve from 0.24339
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Epoch 8: loss did not improve from 0.24339
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Epoch 8: loss did not improve from 0.24339
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Epoch 8: loss did not improve from 0.24339
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303/400 [=====================>........] - ETA: 1:52 - loss: 0.3733 - dice_coefficient: 0.7874
Epoch 8: loss did not improve from 0.24339
304/400 [=====================>........] - ETA: 1:51 - loss: 0.3731 - dice_coefficient: 0.7873
Epoch 8: loss did not improve from 0.24339
305/400 [=====================>........] - ETA: 1:50 - loss: 0.3727 - dice_coefficient: 0.7874
Epoch 8: loss did not improve from 0.24339
306/400 [=====================>........] - ETA: 1:49 - loss: 0.3719 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
307/400 [======================>.......] - ETA: 1:48 - loss: 0.3717 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
308/400 [======================>.......] - ETA: 1:47 - loss: 0.3743 - dice_coefficient: 0.7863
Epoch 8: loss did not improve from 0.24339
309/400 [======================>.......] - ETA: 1:45 - loss: 0.3735 - dice_coefficient: 0.7866
Epoch 8: loss did not improve from 0.24339
310/400 [======================>.......] - ETA: 1:44 - loss: 0.3731 - dice_coefficient: 0.7869
Epoch 8: loss did not improve from 0.24339
311/400 [======================>.......] - ETA: 1:43 - loss: 0.3724 - dice_coefficient: 0.7873
Epoch 8: loss did not improve from 0.24339
312/400 [======================>.......] - ETA: 1:42 - loss: 0.3717 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
313/400 [======================>.......] - ETA: 1:41 - loss: 0.3719 - dice_coefficient: 0.7873
Epoch 8: loss did not improve from 0.24339
314/400 [======================>.......] - ETA: 1:39 - loss: 0.3718 - dice_coefficient: 0.7874
Epoch 8: loss did not improve from 0.24339
315/400 [======================>.......] - ETA: 1:38 - loss: 0.3713 - dice_coefficient: 0.7876
Epoch 8: loss did not improve from 0.24339
316/400 [======================>.......] - ETA: 1:37 - loss: 0.3709 - dice_coefficient: 0.7877
Epoch 8: loss did not improve from 0.24339
317/400 [======================>.......] - ETA: 1:36 - loss: 0.3706 - dice_coefficient: 0.7877
Epoch 8: loss did not improve from 0.24339
318/400 [======================>.......] - ETA: 1:35 - loss: 0.3701 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
319/400 [======================>.......] - ETA: 1:33 - loss: 0.3699 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
320/400 [=======================>......] - ETA: 1:32 - loss: 0.3693 - dice_coefficient: 0.7881
Epoch 8: loss did not improve from 0.24339
321/400 [=======================>......] - ETA: 1:31 - loss: 0.3696 - dice_coefficient: 0.7877
Epoch 8: loss did not improve from 0.24339
322/400 [=======================>......] - ETA: 1:30 - loss: 0.3691 - dice_coefficient: 0.7878
Epoch 8: loss did not improve from 0.24339
323/400 [=======================>......] - ETA: 1:29 - loss: 0.3685 - dice_coefficient: 0.7882
Epoch 8: loss did not improve from 0.24339
324/400 [=======================>......] - ETA: 1:28 - loss: 0.3683 - dice_coefficient: 0.7881
Epoch 8: loss did not improve from 0.24339
325/400 [=======================>......] - ETA: 1:26 - loss: 0.3680 - dice_coefficient: 0.7881
Epoch 8: loss did not improve from 0.24339
326/400 [=======================>......] - ETA: 1:25 - loss: 0.3674 - dice_coefficient: 0.7884
Epoch 8: loss did not improve from 0.24339
327/400 [=======================>......] - ETA: 1:24 - loss: 0.3667 - dice_coefficient: 0.7887
Epoch 8: loss did not improve from 0.24339
328/400 [=======================>......] - ETA: 1:23 - loss: 0.3662 - dice_coefficient: 0.7890
Epoch 8: loss did not improve from 0.24339
329/400 [=======================>......] - ETA: 1:22 - loss: 0.3660 - dice_coefficient: 0.7890
Epoch 8: loss did not improve from 0.24339
330/400 [=======================>......] - ETA: 1:21 - loss: 0.3655 - dice_coefficient: 0.7893
Epoch 8: loss did not improve from 0.24339
331/400 [=======================>......] - ETA: 1:20 - loss: 0.3653 - dice_coefficient: 0.7895
Epoch 8: loss did not improve from 0.24339
332/400 [=======================>......] - ETA: 1:19 - loss: 0.3651 - dice_coefficient: 0.7897
Epoch 8: loss did not improve from 0.24339
333/400 [=======================>......] - ETA: 1:17 - loss: 0.3670 - dice_coefficient: 0.7885
Epoch 8: loss did not improve from 0.24339
334/400 [========================>.....] - ETA: 1:16 - loss: 0.3669 - dice_coefficient: 0.7883
Epoch 8: loss did not improve from 0.24339
335/400 [========================>.....] - ETA: 1:15 - loss: 0.3665 - dice_coefficient: 0.7885
Epoch 8: loss did not improve from 0.24339
336/400 [========================>.....] - ETA: 1:14 - loss: 0.3657 - dice_coefficient: 0.7889
Epoch 8: loss did not improve from 0.24339
337/400 [========================>.....] - ETA: 1:13 - loss: 0.3652 - dice_coefficient: 0.7891
Epoch 8: loss did not improve from 0.24339
338/400 [========================>.....] - ETA: 1:11 - loss: 0.3648 - dice_coefficient: 0.7894
Epoch 8: loss did not improve from 0.24339
339/400 [========================>.....] - ETA: 1:10 - loss: 0.3651 - dice_coefficient: 0.7889
Epoch 8: loss did not improve from 0.24339
340/400 [========================>.....] - ETA: 1:09 - loss: 0.3655 - dice_coefficient: 0.7889
Epoch 8: loss did not improve from 0.24339
341/400 [========================>.....] - ETA: 1:08 - loss: 0.3650 - dice_coefficient: 0.7891
Epoch 8: loss did not improve from 0.24339
342/400 [========================>.....] - ETA: 1:07 - loss: 0.3649 - dice_coefficient: 0.7889
Epoch 8: loss did not improve from 0.24339
343/400 [========================>.....] - ETA: 1:06 - loss: 0.3642 - dice_coefficient: 0.7893
Epoch 8: loss did not improve from 0.24339
344/400 [========================>.....] - ETA: 1:05 - loss: 0.3638 - dice_coefficient: 0.7896
Epoch 8: loss did not improve from 0.24339
345/400 [========================>.....] - ETA: 1:03 - loss: 0.3631 - dice_coefficient: 0.7900
Epoch 8: loss did not improve from 0.24339
346/400 [========================>.....] - ETA: 1:02 - loss: 0.3629 - dice_coefficient: 0.7899
Epoch 8: loss did not improve from 0.24339
347/400 [=========================>....] - ETA: 1:01 - loss: 0.3622 - dice_coefficient: 0.7902
Epoch 8: loss did not improve from 0.24339
348/400 [=========================>....] - ETA: 1:00 - loss: 0.3620 - dice_coefficient: 0.7902
Epoch 8: loss did not improve from 0.24339
349/400 [=========================>....] - ETA: 59s - loss: 0.3621 - dice_coefficient: 0.7904
Epoch 8: loss did not improve from 0.24339
350/400 [=========================>....] - ETA: 58s - loss: 0.3617 - dice_coefficient: 0.7906
Epoch 8: loss did not improve from 0.24339
351/400 [=========================>....] - ETA: 56s - loss: 0.3614 - dice_coefficient: 0.7906
Epoch 8: loss did not improve from 0.24339
352/400 [=========================>....] - ETA: 55s - loss: 0.3622 - dice_coefficient: 0.7899
Epoch 8: loss did not improve from 0.24339
353/400 [=========================>....] - ETA: 54s - loss: 0.3615 - dice_coefficient: 0.7903
Epoch 8: loss did not improve from 0.24339
354/400 [=========================>....] - ETA: 53s - loss: 0.3630 - dice_coefficient: 0.7893
Epoch 8: loss did not improve from 0.24339
355/400 [=========================>....] - ETA: 52s - loss: 0.3630 - dice_coefficient: 0.7891
Epoch 8: loss did not improve from 0.24339
356/400 [=========================>....] - ETA: 51s - loss: 0.3626 - dice_coefficient: 0.7892
Epoch 8: loss did not improve from 0.24339
357/400 [=========================>....] - ETA: 49s - loss: 0.3621 - dice_coefficient: 0.7894
Epoch 8: loss did not improve from 0.24339
358/400 [=========================>....] - ETA: 48s - loss: 0.3613 - dice_coefficient: 0.7898
Epoch 8: loss did not improve from 0.24339
359/400 [=========================>....] - ETA: 47s - loss: 0.3614 - dice_coefficient: 0.7896
Epoch 8: loss did not improve from 0.24339
360/400 [==========================>...] - ETA: 46s - loss: 0.3608 - dice_coefficient: 0.7899
Epoch 8: loss did not improve from 0.24339
361/400 [==========================>...] - ETA: 45s - loss: 0.3603 - dice_coefficient: 0.7902
Epoch 8: loss did not improve from 0.24339
362/400 [==========================>...] - ETA: 44s - loss: 0.3599 - dice_coefficient: 0.7903
Epoch 8: loss did not improve from 0.24339
363/400 [==========================>...] - ETA: 42s - loss: 0.3598 - dice_coefficient: 0.7903
Epoch 8: loss did not improve from 0.24339
364/400 [==========================>...] - ETA: 41s - loss: 0.3593 - dice_coefficient: 0.7904
Epoch 8: loss did not improve from 0.24339
365/400 [==========================>...] - ETA: 40s - loss: 0.3591 - dice_coefficient: 0.7907
Epoch 8: loss did not improve from 0.24339
366/400 [==========================>...] - ETA: 39s - loss: 0.3605 - dice_coefficient: 0.7897
Epoch 8: loss did not improve from 0.24339
367/400 [==========================>...] - ETA: 38s - loss: 0.3599 - dice_coefficient: 0.7901
Epoch 8: loss did not improve from 0.24339
368/400 [==========================>...] - ETA: 37s - loss: 0.3595 - dice_coefficient: 0.7903
Epoch 8: loss did not improve from 0.24339
369/400 [==========================>...] - ETA: 36s - loss: 0.3593 - dice_coefficient: 0.7903
Epoch 8: loss did not improve from 0.24339
370/400 [==========================>...] - ETA: 34s - loss: 0.3588 - dice_coefficient: 0.7905
Epoch 8: loss did not improve from 0.24339
371/400 [==========================>...] - ETA: 33s - loss: 0.3583 - dice_coefficient: 0.7907
Epoch 8: loss did not improve from 0.24339
372/400 [==========================>...] - ETA: 32s - loss: 0.3578 - dice_coefficient: 0.7910
Epoch 8: loss did not improve from 0.24339
373/400 [==========================>...] - ETA: 31s - loss: 0.3572 - dice_coefficient: 0.7913
Epoch 8: loss did not improve from 0.24339
374/400 [===========================>..] - ETA: 30s - loss: 0.3567 - dice_coefficient: 0.7916
Epoch 8: loss did not improve from 0.24339
375/400 [===========================>..] - ETA: 29s - loss: 0.3562 - dice_coefficient: 0.7919
Epoch 8: loss did not improve from 0.24339
376/400 [===========================>..] - ETA: 27s - loss: 0.3559 - dice_coefficient: 0.7921
Epoch 8: loss did not improve from 0.24339
377/400 [===========================>..] - ETA: 26s - loss: 0.3558 - dice_coefficient: 0.7920
Epoch 8: loss did not improve from 0.24339
378/400 [===========================>..] - ETA: 25s - loss: 0.3555 - dice_coefficient: 0.7921
Epoch 8: loss did not improve from 0.24339
379/400 [===========================>..] - ETA: 24s - loss: 0.3549 - dice_coefficient: 0.7925
Epoch 8: loss did not improve from 0.24339
380/400 [===========================>..] - ETA: 23s - loss: 0.3546 - dice_coefficient: 0.7925
Epoch 8: loss did not improve from 0.24339
381/400 [===========================>..] - ETA: 22s - loss: 0.3541 - dice_coefficient: 0.7929
Epoch 8: loss did not improve from 0.24339
382/400 [===========================>..] - ETA: 20s - loss: 0.3535 - dice_coefficient: 0.7932
Epoch 8: loss did not improve from 0.24339
383/400 [===========================>..] - ETA: 19s - loss: 0.3534 - dice_coefficient: 0.7933
Epoch 8: loss did not improve from 0.24339
384/400 [===========================>..] - ETA: 18s - loss: 0.3532 - dice_coefficient: 0.7933
Epoch 8: loss did not improve from 0.24339
385/400 [===========================>..] - ETA: 17s - loss: 0.3528 - dice_coefficient: 0.7935
Epoch 8: loss did not improve from 0.24339
386/400 [===========================>..] - ETA: 16s - loss: 0.3524 - dice_coefficient: 0.7936
Epoch 8: loss did not improve from 0.24339
387/400 [============================>.] - ETA: 15s - loss: 0.3534 - dice_coefficient: 0.7929
Epoch 8: loss did not improve from 0.24339
388/400 [============================>.] - ETA: 13s - loss: 0.3542 - dice_coefficient: 0.7924
Epoch 8: loss did not improve from 0.24339
389/400 [============================>.] - ETA: 12s - loss: 0.3538 - dice_coefficient: 0.7925
Epoch 8: loss did not improve from 0.24339
390/400 [============================>.] - ETA: 11s - loss: 0.3548 - dice_coefficient: 0.7918
Epoch 8: loss did not improve from 0.24339
391/400 [============================>.] - ETA: 10s - loss: 0.3544 - dice_coefficient: 0.7919
Epoch 8: loss did not improve from 0.24339
392/400 [============================>.] - ETA: 9s - loss: 0.3541 - dice_coefficient: 0.7919
Epoch 8: loss did not improve from 0.24339
393/400 [============================>.] - ETA: 8s - loss: 0.3537 - dice_coefficient: 0.7922
Epoch 8: loss did not improve from 0.24339
394/400 [============================>.] - ETA: 6s - loss: 0.3534 - dice_coefficient: 0.7924
Epoch 8: loss did not improve from 0.24339
395/400 [============================>.] - ETA: 5s - loss: 0.3531 - dice_coefficient: 0.7924
Epoch 8: loss did not improve from 0.24339
396/400 [============================>.] - ETA: 4s - loss: 0.3527 - dice_coefficient: 0.7927
Epoch 8: loss did not improve from 0.24339
397/400 [============================>.] - ETA: 3s - loss: 0.3531 - dice_coefficient: 0.7922
Epoch 8: loss did not improve from 0.24339
398/400 [============================>.] - ETA: 2s - loss: 0.3533 - dice_coefficient: 0.7919
Epoch 8: loss did not improve from 0.24339
399/400 [============================>.] - ETA: 1s - loss: 0.3528 - dice_coefficient: 0.7922
Epoch 8: loss did not improve from 0.24339
400/400 [==============================] - 468s 1s/step - loss: 0.3525 - dice_coefficient: 0.7924 - val_loss: 0.8695 - val_dice_coefficient: 0.5433 - lr: 1.0000e-04
Epoch 9/10
Epoch 9: loss did not improve from 0.24339
1/400 [..............................] - ETA: 7:05 - loss: 0.4300 - dice_coefficient: 0.6653
Epoch 9: loss did not improve from 0.24339
2/400 [..............................] - ETA: 7:23 - loss: 0.3239 - dice_coefficient: 0.7613
Epoch 9: loss did not improve from 0.24339
3/400 [..............................] - ETA: 7:11 - loss: 0.2997 - dice_coefficient: 0.7821
Epoch 9: loss did not improve from 0.24339
4/400 [..............................] - ETA: 7:07 - loss: 0.2525 - dice_coefficient: 0.8186
Epoch 9: loss did not improve from 0.24339
5/400 [..............................] - ETA: 7:05 - loss: 0.2476 - dice_coefficient: 0.8319
Epoch 9: loss did not improve from 0.24339
6/400 [..............................] - ETA: 7:05 - loss: 0.2460 - dice_coefficient: 0.8291
Epoch 9: loss did not improve from 0.24339
7/400 [..............................] - ETA: 7:00 - loss: 0.2701 - dice_coefficient: 0.8079
Epoch 9: loss did not improve from 0.24339
8/400 [..............................] - ETA: 6:59 - loss: 0.2542 - dice_coefficient: 0.8198
Epoch 9: loss did not improve from 0.24339
9/400 [..............................] - ETA: 6:57 - loss: 0.2727 - dice_coefficient: 0.8037
Epoch 9: loss did not improve from 0.24339
10/400 [..............................] - ETA: 7:30 - loss: 0.2982 - dice_coefficient: 0.7847
Epoch 9: loss did not improve from 0.24339
11/400 [..............................] - ETA: 7:49 - loss: 0.2923 - dice_coefficient: 0.7891
Epoch 9: loss did not improve from 0.24339
12/400 [..............................] - ETA: 7:43 - loss: 0.2783 - dice_coefficient: 0.7994
Epoch 9: loss did not improve from 0.24339
13/400 [..............................] - ETA: 7:36 - loss: 0.2669 - dice_coefficient: 0.8083
Epoch 9: loss did not improve from 0.24339
14/400 [>.............................] - ETA: 7:28 - loss: 0.2647 - dice_coefficient: 0.8090
Epoch 9: loss did not improve from 0.24339
15/400 [>.............................] - ETA: 7:21 - loss: 0.2640 - dice_coefficient: 0.8091
Epoch 9: loss did not improve from 0.24339
16/400 [>.............................] - ETA: 7:19 - loss: 0.2613 - dice_coefficient: 0.8129
Epoch 9: loss did not improve from 0.24339
17/400 [>.............................] - ETA: 7:13 - loss: 0.2530 - dice_coefficient: 0.8200
Epoch 9: loss improved from 0.24339 to 0.24326, saving model to model_0.24.h5
18/400 [>.............................] - ETA: 7:20 - loss: 0.2433 - dice_coefficient: 0.8271
Epoch 9: loss did not improve from 0.24326
19/400 [>.............................] - ETA: 7:24 - loss: 0.2433 - dice_coefficient: 0.8265
Epoch 9: loss improved from 0.24326 to 0.23739, saving model to model_0.24.h5
20/400 [>.............................] - ETA: 7:35 - loss: 0.2374 - dice_coefficient: 0.8317
Epoch 9: loss did not improve from 0.23739
21/400 [>.............................] - ETA: 7:44 - loss: 0.2647 - dice_coefficient: 0.8139
Epoch 9: loss did not improve from 0.23739
22/400 [>.............................] - ETA: 7:46 - loss: 0.2589 - dice_coefficient: 0.8192
Epoch 9: loss did not improve from 0.23739
23/400 [>.............................] - ETA: 7:43 - loss: 0.2586 - dice_coefficient: 0.8200
Epoch 9: loss did not improve from 0.23739
24/400 [>.............................] - ETA: 7:39 - loss: 0.2543 - dice_coefficient: 0.8231
Epoch 9: loss did not improve from 0.23739
25/400 [>.............................] - ETA: 7:37 - loss: 0.2551 - dice_coefficient: 0.8230
Epoch 9: loss did not improve from 0.23739
26/400 [>.............................] - ETA: 7:31 - loss: 0.2544 - dice_coefficient: 0.8223
Epoch 9: loss did not improve from 0.23739
27/400 [=>............................] - ETA: 7:27 - loss: 0.2554 - dice_coefficient: 0.8231
Epoch 9: loss did not improve from 0.23739
28/400 [=>............................] - ETA: 7:22 - loss: 0.2497 - dice_coefficient: 0.8272
Epoch 9: loss did not improve from 0.23739
29/400 [=>............................] - ETA: 7:17 - loss: 0.2527 - dice_coefficient: 0.8244
Epoch 9: loss did not improve from 0.23739
30/400 [=>............................] - ETA: 7:13 - loss: 0.2537 - dice_coefficient: 0.8231
Epoch 9: loss did not improve from 0.23739
31/400 [=>............................] - ETA: 7:11 - loss: 0.2708 - dice_coefficient: 0.8122
Epoch 9: loss did not improve from 0.23739
32/400 [=>............................] - ETA: 7:12 - loss: 0.2729 - dice_coefficient: 0.8100
Epoch 9: loss did not improve from 0.23739
33/400 [=>............................] - ETA: 7:19 - loss: 0.2690 - dice_coefficient: 0.8125
Epoch 9: loss did not improve from 0.23739
34/400 [=>............................] - ETA: 7:19 - loss: 0.2650 - dice_coefficient: 0.8150
Epoch 9: loss did not improve from 0.23739
35/400 [=>............................] - ETA: 7:16 - loss: 0.2626 - dice_coefficient: 0.8159
Epoch 9: loss did not improve from 0.23739
36/400 [=>............................] - ETA: 7:11 - loss: 0.2617 - dice_coefficient: 0.8171
Epoch 9: loss did not improve from 0.23739
37/400 [=>............................] - ETA: 7:09 - loss: 0.2624 - dice_coefficient: 0.8161
Epoch 9: loss did not improve from 0.23739
38/400 [=>............................] - ETA: 7:06 - loss: 0.2629 - dice_coefficient: 0.8172
Epoch 9: loss did not improve from 0.23739
39/400 [=>............................] - ETA: 7:04 - loss: 0.2590 - dice_coefficient: 0.8202
Epoch 9: loss did not improve from 0.23739
40/400 [==>...........................] - ETA: 7:01 - loss: 0.2650 - dice_coefficient: 0.8156
Epoch 9: loss did not improve from 0.23739
41/400 [==>...........................] - ETA: 6:59 - loss: 0.2634 - dice_coefficient: 0.8171
Epoch 9: loss did not improve from 0.23739
42/400 [==>...........................] - ETA: 6:57 - loss: 0.2664 - dice_coefficient: 0.8181
Epoch 9: loss did not improve from 0.23739
43/400 [==>...........................] - ETA: 6:54 - loss: 0.2623 - dice_coefficient: 0.8210
Epoch 9: loss did not improve from 0.23739
44/400 [==>...........................] - ETA: 6:54 - loss: 0.2597 - dice_coefficient: 0.8231
Epoch 9: loss did not improve from 0.23739
45/400 [==>...........................] - ETA: 6:57 - loss: 0.2581 - dice_coefficient: 0.8244
Epoch 9: loss did not improve from 0.23739
46/400 [==>...........................] - ETA: 6:59 - loss: 0.2647 - dice_coefficient: 0.8192
Epoch 9: loss did not improve from 0.23739
47/400 [==>...........................] - ETA: 6:57 - loss: 0.2672 - dice_coefficient: 0.8168
Epoch 9: loss did not improve from 0.23739
48/400 [==>...........................] - ETA: 6:55 - loss: 0.2657 - dice_coefficient: 0.8177
Epoch 9: loss did not improve from 0.23739
49/400 [==>...........................] - ETA: 6:52 - loss: 0.2625 - dice_coefficient: 0.8199
Epoch 9: loss did not improve from 0.23739
50/400 [==>...........................] - ETA: 6:50 - loss: 0.2605 - dice_coefficient: 0.8217
Epoch 9: loss did not improve from 0.23739
51/400 [==>...........................] - ETA: 6:48 - loss: 0.2598 - dice_coefficient: 0.8218
Epoch 9: loss did not improve from 0.23739
52/400 [==>...........................] - ETA: 6:45 - loss: 0.2579 - dice_coefficient: 0.8233
Epoch 9: loss did not improve from 0.23739
53/400 [==>...........................] - ETA: 6:43 - loss: 0.2560 - dice_coefficient: 0.8250
Epoch 9: loss did not improve from 0.23739
54/400 [===>..........................] - ETA: 6:40 - loss: 0.2550 - dice_coefficient: 0.8253
Epoch 9: loss did not improve from 0.23739
55/400 [===>..........................] - ETA: 6:38 - loss: 0.2522 - dice_coefficient: 0.8273
Epoch 9: loss did not improve from 0.23739
56/400 [===>..........................] - ETA: 6:36 - loss: 0.2509 - dice_coefficient: 0.8289
Epoch 9: loss did not improve from 0.23739
57/400 [===>..........................] - ETA: 6:39 - loss: 0.2514 - dice_coefficient: 0.8282
Epoch 9: loss did not improve from 0.23739
58/400 [===>..........................] - ETA: 6:40 - loss: 0.2492 - dice_coefficient: 0.8298
Epoch 9: loss did not improve from 0.23739
59/400 [===>..........................] - ETA: 6:38 - loss: 0.2479 - dice_coefficient: 0.8306
Epoch 9: loss did not improve from 0.23739
60/400 [===>..........................] - ETA: 6:36 - loss: 0.2469 - dice_coefficient: 0.8314
Epoch 9: loss did not improve from 0.23739
61/400 [===>..........................] - ETA: 6:34 - loss: 0.2472 - dice_coefficient: 0.8317
Epoch 9: loss did not improve from 0.23739
62/400 [===>..........................] - ETA: 6:32 - loss: 0.2458 - dice_coefficient: 0.8330
Epoch 9: loss did not improve from 0.23739
63/400 [===>..........................] - ETA: 6:29 - loss: 0.2468 - dice_coefficient: 0.8318
Epoch 9: loss did not improve from 0.23739
64/400 [===>..........................] - ETA: 6:27 - loss: 0.2480 - dice_coefficient: 0.8305
Epoch 9: loss did not improve from 0.23739
65/400 [===>..........................] - ETA: 6:25 - loss: 0.2474 - dice_coefficient: 0.8309
Epoch 9: loss did not improve from 0.23739
66/400 [===>..........................] - ETA: 6:24 - loss: 0.2462 - dice_coefficient: 0.8322
Epoch 9: loss did not improve from 0.23739
67/400 [====>.........................] - ETA: 6:22 - loss: 0.2445 - dice_coefficient: 0.8332
Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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Epoch 9: loss did not improve from 0.23739
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396/400 [============================>.] - ETA: 4s - loss: 0.2989 - dice_coefficient: 0.8248
Epoch 9: loss did not improve from 0.23739
397/400 [============================>.] - ETA: 3s - loss: 0.2995 - dice_coefficient: 0.8242
Epoch 9: loss did not improve from 0.23739
398/400 [============================>.] - ETA: 2s - loss: 0.2997 - dice_coefficient: 0.8240
Epoch 9: loss did not improve from 0.23739
399/400 [============================>.] - ETA: 1s - loss: 0.2994 - dice_coefficient: 0.8241
Epoch 9: loss did not improve from 0.23739
400/400 [==============================] - 460s 1s/step - loss: 0.2999 - dice_coefficient: 0.8236 - val_loss: 0.7964 - val_dice_coefficient: 0.5729 - lr: 1.0000e-04
Epoch 10/10
Epoch 10: loss improved from 0.23739 to 0.19646, saving model to model_0.20.h5
1/400 [..............................] - ETA: 13:45 - loss: 0.1965 - dice_coefficient: 0.8932
Epoch 10: loss improved from 0.19646 to 0.18131, saving model to model_0.18.h5
2/400 [..............................] - ETA: 14:23 - loss: 0.1813 - dice_coefficient: 0.8878
Epoch 10: loss did not improve from 0.18131
3/400 [..............................] - ETA: 11:06 - loss: 0.2210 - dice_coefficient: 0.8434
Epoch 10: loss did not improve from 0.18131
4/400 [..............................] - ETA: 9:40 - loss: 0.2577 - dice_coefficient: 0.8483
Epoch 10: loss did not improve from 0.18131
5/400 [..............................] - ETA: 8:47 - loss: 0.2771 - dice_coefficient: 0.8224
Epoch 10: loss did not improve from 0.18131
6/400 [..............................] - ETA: 8:20 - loss: 0.2528 - dice_coefficient: 0.8405
Epoch 10: loss did not improve from 0.18131
7/400 [..............................] - ETA: 7:58 - loss: 0.2381 - dice_coefficient: 0.8475
Epoch 10: loss did not improve from 0.18131
8/400 [..............................] - ETA: 7:48 - loss: 0.2244 - dice_coefficient: 0.8554
Epoch 10: loss did not improve from 0.18131
9/400 [..............................] - ETA: 7:37 - loss: 0.2831 - dice_coefficient: 0.8150
Epoch 10: loss did not improve from 0.18131
10/400 [..............................] - ETA: 7:31 - loss: 0.2769 - dice_coefficient: 0.8162
Epoch 10: loss did not improve from 0.18131
11/400 [..............................] - ETA: 7:24 - loss: 0.2626 - dice_coefficient: 0.8259
Epoch 10: loss did not improve from 0.18131
12/400 [..............................] - ETA: 7:37 - loss: 0.2618 - dice_coefficient: 0.8251
Epoch 10: loss did not improve from 0.18131
13/400 [..............................] - ETA: 7:53 - loss: 0.2503 - dice_coefficient: 0.8319
Epoch 10: loss did not improve from 0.18131
14/400 [>.............................] - ETA: 7:53 - loss: 0.2447 - dice_coefficient: 0.8351
Epoch 10: loss did not improve from 0.18131
15/400 [>.............................] - ETA: 7:45 - loss: 0.2455 - dice_coefficient: 0.8328
Epoch 10: loss did not improve from 0.18131
16/400 [>.............................] - ETA: 7:39 - loss: 0.2414 - dice_coefficient: 0.8376
Epoch 10: loss did not improve from 0.18131
17/400 [>.............................] - ETA: 7:33 - loss: 0.2367 - dice_coefficient: 0.8422
Epoch 10: loss did not improve from 0.18131
18/400 [>.............................] - ETA: 7:25 - loss: 0.2405 - dice_coefficient: 0.8374
Epoch 10: loss did not improve from 0.18131
19/400 [>.............................] - ETA: 7:19 - loss: 0.2334 - dice_coefficient: 0.8427
Epoch 10: loss did not improve from 0.18131
20/400 [>.............................] - ETA: 7:14 - loss: 0.2281 - dice_coefficient: 0.8459
Epoch 10: loss did not improve from 0.18131
21/400 [>.............................] - ETA: 7:11 - loss: 0.2208 - dice_coefficient: 0.8510
Epoch 10: loss did not improve from 0.18131
22/400 [>.............................] - ETA: 7:07 - loss: 0.2214 - dice_coefficient: 0.8494
Epoch 10: loss did not improve from 0.18131
23/400 [>.............................] - ETA: 7:02 - loss: 0.2451 - dice_coefficient: 0.8340
Epoch 10: loss did not improve from 0.18131
24/400 [>.............................] - ETA: 7:05 - loss: 0.2450 - dice_coefficient: 0.8332
Epoch 10: loss did not improve from 0.18131
25/400 [>.............................] - ETA: 7:15 - loss: 0.2414 - dice_coefficient: 0.8363
Epoch 10: loss did not improve from 0.18131
26/400 [>.............................] - ETA: 7:17 - loss: 0.2388 - dice_coefficient: 0.8386
Epoch 10: loss did not improve from 0.18131
27/400 [=>............................] - ETA: 7:13 - loss: 0.2523 - dice_coefficient: 0.8283
Epoch 10: loss did not improve from 0.18131
28/400 [=>............................] - ETA: 7:11 - loss: 0.2492 - dice_coefficient: 0.8315
Epoch 10: loss did not improve from 0.18131
29/400 [=>............................] - ETA: 7:08 - loss: 0.2482 - dice_coefficient: 0.8315
Epoch 10: loss did not improve from 0.18131
30/400 [=>............................] - ETA: 7:03 - loss: 0.2453 - dice_coefficient: 0.8334
Epoch 10: loss did not improve from 0.18131
31/400 [=>............................] - ETA: 7:00 - loss: 0.2443 - dice_coefficient: 0.8357
Epoch 10: loss did not improve from 0.18131
32/400 [=>............................] - ETA: 6:56 - loss: 0.2642 - dice_coefficient: 0.8229
Epoch 10: loss did not improve from 0.18131
33/400 [=>............................] - ETA: 6:53 - loss: 0.2640 - dice_coefficient: 0.8242
Epoch 10: loss did not improve from 0.18131
34/400 [=>............................] - ETA: 6:50 - loss: 0.2667 - dice_coefficient: 0.8212
Epoch 10: loss did not improve from 0.18131
35/400 [=>............................] - ETA: 6:48 - loss: 0.2658 - dice_coefficient: 0.8216
Epoch 10: loss did not improve from 0.18131
36/400 [=>............................] - ETA: 6:46 - loss: 0.2620 - dice_coefficient: 0.8238
Epoch 10: loss did not improve from 0.18131
37/400 [=>............................] - ETA: 6:52 - loss: 0.2604 - dice_coefficient: 0.8244
Epoch 10: loss did not improve from 0.18131
38/400 [=>............................] - ETA: 7:01 - loss: 0.2639 - dice_coefficient: 0.8254
Epoch 10: loss did not improve from 0.18131
39/400 [=>............................] - ETA: 7:01 - loss: 0.2601 - dice_coefficient: 0.8278
Epoch 10: loss did not improve from 0.18131
40/400 [==>...........................] - ETA: 6:58 - loss: 0.2570 - dice_coefficient: 0.8299
Epoch 10: loss did not improve from 0.18131
41/400 [==>...........................] - ETA: 6:56 - loss: 0.2593 - dice_coefficient: 0.8274
Epoch 10: loss did not improve from 0.18131
42/400 [==>...........................] - ETA: 6:53 - loss: 0.2591 - dice_coefficient: 0.8280
Epoch 10: loss did not improve from 0.18131
43/400 [==>...........................] - ETA: 6:50 - loss: 0.2555 - dice_coefficient: 0.8302
Epoch 10: loss did not improve from 0.18131
44/400 [==>...........................] - ETA: 6:48 - loss: 0.2564 - dice_coefficient: 0.8303
Epoch 10: loss did not improve from 0.18131
45/400 [==>...........................] - ETA: 6:46 - loss: 0.2549 - dice_coefficient: 0.8309
Epoch 10: loss did not improve from 0.18131
46/400 [==>...........................] - ETA: 6:43 - loss: 0.2562 - dice_coefficient: 0.8301
Epoch 10: loss did not improve from 0.18131
47/400 [==>...........................] - ETA: 6:41 - loss: 0.2548 - dice_coefficient: 0.8307
Epoch 10: loss did not improve from 0.18131
48/400 [==>...........................] - ETA: 6:40 - loss: 0.2527 - dice_coefficient: 0.8320
Epoch 10: loss did not improve from 0.18131
49/400 [==>...........................] - ETA: 6:44 - loss: 0.2501 - dice_coefficient: 0.8337
Epoch 10: loss did not improve from 0.18131
50/400 [==>...........................] - ETA: 6:45 - loss: 0.2493 - dice_coefficient: 0.8349
Epoch 10: loss did not improve from 0.18131
51/400 [==>...........................] - ETA: 6:43 - loss: 0.2465 - dice_coefficient: 0.8367
Epoch 10: loss did not improve from 0.18131
52/400 [==>...........................] - ETA: 6:41 - loss: 0.2434 - dice_coefficient: 0.8388
Epoch 10: loss did not improve from 0.18131
53/400 [==>...........................] - ETA: 6:39 - loss: 0.2419 - dice_coefficient: 0.8394
Epoch 10: loss did not improve from 0.18131
54/400 [===>..........................] - ETA: 6:37 - loss: 0.2419 - dice_coefficient: 0.8388
Epoch 10: loss did not improve from 0.18131
55/400 [===>..........................] - ETA: 6:35 - loss: 0.2429 - dice_coefficient: 0.8381
Epoch 10: loss did not improve from 0.18131
56/400 [===>..........................] - ETA: 6:33 - loss: 0.2426 - dice_coefficient: 0.8389
Epoch 10: loss did not improve from 0.18131
57/400 [===>..........................] - ETA: 6:31 - loss: 0.2506 - dice_coefficient: 0.8331
Epoch 10: loss did not improve from 0.18131
58/400 [===>..........................] - ETA: 6:28 - loss: 0.2593 - dice_coefficient: 0.8274
Epoch 10: loss did not improve from 0.18131
59/400 [===>..........................] - ETA: 6:26 - loss: 0.2582 - dice_coefficient: 0.8287
Epoch 10: loss did not improve from 0.18131
60/400 [===>..........................] - ETA: 6:25 - loss: 0.2578 - dice_coefficient: 0.8293
Epoch 10: loss did not improve from 0.18131
61/400 [===>..........................] - ETA: 6:27 - loss: 0.2607 - dice_coefficient: 0.8265
Epoch 10: loss did not improve from 0.18131
62/400 [===>..........................] - ETA: 6:28 - loss: 0.2674 - dice_coefficient: 0.8216
Epoch 10: loss did not improve from 0.18131
63/400 [===>..........................] - ETA: 6:26 - loss: 0.2669 - dice_coefficient: 0.8215
Epoch 10: loss did not improve from 0.18131
64/400 [===>..........................] - ETA: 6:24 - loss: 0.2664 - dice_coefficient: 0.8215
Epoch 10: loss did not improve from 0.18131
65/400 [===>..........................] - ETA: 6:22 - loss: 0.2661 - dice_coefficient: 0.8212
Epoch 10: loss did not improve from 0.18131
66/400 [===>..........................] - ETA: 6:20 - loss: 0.2649 - dice_coefficient: 0.8225
Epoch 10: loss did not improve from 0.18131
67/400 [====>.........................] - ETA: 6:18 - loss: 0.2630 - dice_coefficient: 0.8240
Epoch 10: loss did not improve from 0.18131
68/400 [====>.........................] - ETA: 6:16 - loss: 0.2621 - dice_coefficient: 0.8244
Epoch 10: loss did not improve from 0.18131
69/400 [====>.........................] - ETA: 6:14 - loss: 0.2616 - dice_coefficient: 0.8251
Epoch 10: loss did not improve from 0.18131
70/400 [====>.........................] - ETA: 6:13 - loss: 0.2603 - dice_coefficient: 0.8259
Epoch 10: loss did not improve from 0.18131
71/400 [====>.........................] - ETA: 6:11 - loss: 0.2580 - dice_coefficient: 0.8275
Epoch 10: loss did not improve from 0.18131
72/400 [====>.........................] - ETA: 6:09 - loss: 0.2565 - dice_coefficient: 0.8283
Epoch 10: loss did not improve from 0.18131
73/400 [====>.........................] - ETA: 6:10 - loss: 0.2541 - dice_coefficient: 0.8300
Epoch 10: loss did not improve from 0.18131
74/400 [====>.........................] - ETA: 6:12 - loss: 0.2526 - dice_coefficient: 0.8311
Epoch 10: loss did not improve from 0.18131
75/400 [====>.........................] - ETA: 6:11 - loss: 0.2545 - dice_coefficient: 0.8293
Epoch 10: loss did not improve from 0.18131
76/400 [====>.........................] - ETA: 6:09 - loss: 0.2523 - dice_coefficient: 0.8307
Epoch 10: loss did not improve from 0.18131
77/400 [====>.........................] - ETA: 6:07 - loss: 0.2511 - dice_coefficient: 0.8313
Epoch 10: loss did not improve from 0.18131
78/400 [====>.........................] - ETA: 6:05 - loss: 0.2502 - dice_coefficient: 0.8325
Epoch 10: loss did not improve from 0.18131
79/400 [====>.........................] - ETA: 6:03 - loss: 0.2500 - dice_coefficient: 0.8322
Epoch 10: loss did not improve from 0.18131
80/400 [=====>........................] - ETA: 6:02 - loss: 0.2490 - dice_coefficient: 0.8331
Epoch 10: loss did not improve from 0.18131
81/400 [=====>........................] - ETA: 6:00 - loss: 0.2474 - dice_coefficient: 0.8343
Epoch 10: loss did not improve from 0.18131
82/400 [=====>........................] - ETA: 5:58 - loss: 0.2470 - dice_coefficient: 0.8345
Epoch 10: loss did not improve from 0.18131
83/400 [=====>........................] - ETA: 5:56 - loss: 0.2470 - dice_coefficient: 0.8344
Epoch 10: loss did not improve from 0.18131
84/400 [=====>........................] - ETA: 5:54 - loss: 0.2477 - dice_coefficient: 0.8340
Epoch 10: loss did not improve from 0.18131
85/400 [=====>........................] - ETA: 5:53 - loss: 0.2461 - dice_coefficient: 0.8349
Epoch 10: loss did not improve from 0.18131
86/400 [=====>........................] - ETA: 5:54 - loss: 0.2447 - dice_coefficient: 0.8358
Epoch 10: loss did not improve from 0.18131
87/400 [=====>........................] - ETA: 5:55 - loss: 0.2451 - dice_coefficient: 0.8353
Epoch 10: loss did not improve from 0.18131
88/400 [=====>........................] - ETA: 5:53 - loss: 0.2462 - dice_coefficient: 0.8340
Epoch 10: loss did not improve from 0.18131
89/400 [=====>........................] - ETA: 5:51 - loss: 0.2451 - dice_coefficient: 0.8348
Epoch 10: loss did not improve from 0.18131
90/400 [=====>........................] - ETA: 5:50 - loss: 0.2436 - dice_coefficient: 0.8358
Epoch 10: loss did not improve from 0.18131
91/400 [=====>........................] - ETA: 5:48 - loss: 0.2429 - dice_coefficient: 0.8367
Epoch 10: loss did not improve from 0.18131
92/400 [=====>........................] - ETA: 5:47 - loss: 0.2432 - dice_coefficient: 0.8363
Epoch 10: loss did not improve from 0.18131
93/400 [=====>........................] - ETA: 5:45 - loss: 0.2422 - dice_coefficient: 0.8372
Epoch 10: loss did not improve from 0.18131
94/400 [======>.......................] - ETA: 5:43 - loss: 0.2408 - dice_coefficient: 0.8382
Epoch 10: loss did not improve from 0.18131
95/400 [======>.......................] - ETA: 5:42 - loss: 0.2404 - dice_coefficient: 0.8389
Epoch 10: loss did not improve from 0.18131
96/400 [======>.......................] - ETA: 5:41 - loss: 0.2398 - dice_coefficient: 0.8396
Epoch 10: loss did not improve from 0.18131
97/400 [======>.......................] - ETA: 5:39 - loss: 0.2391 - dice_coefficient: 0.8401
Epoch 10: loss did not improve from 0.18131
98/400 [======>.......................] - ETA: 5:40 - loss: 0.2386 - dice_coefficient: 0.8408
Epoch 10: loss did not improve from 0.18131
99/400 [======>.......................] - ETA: 5:41 - loss: 0.2399 - dice_coefficient: 0.8396
Epoch 10: loss did not improve from 0.18131
100/400 [======>.......................] - ETA: 5:39 - loss: 0.2400 - dice_coefficient: 0.8393
Epoch 10: loss did not improve from 0.18131
101/400 [======>.......................] - ETA: 5:38 - loss: 0.2405 - dice_coefficient: 0.8385
Epoch 10: loss did not improve from 0.18131
102/400 [======>.......................] - ETA: 5:37 - loss: 0.2393 - dice_coefficient: 0.8394
Epoch 10: loss did not improve from 0.18131
103/400 [======>.......................] - ETA: 5:35 - loss: 0.2389 - dice_coefficient: 0.8397
Epoch 10: loss did not improve from 0.18131
104/400 [======>.......................] - ETA: 5:33 - loss: 0.2376 - dice_coefficient: 0.8407
Epoch 10: loss did not improve from 0.18131
105/400 [======>.......................] - ETA: 5:32 - loss: 0.2378 - dice_coefficient: 0.8405
Epoch 10: loss did not improve from 0.18131
106/400 [======>.......................] - ETA: 5:30 - loss: 0.2372 - dice_coefficient: 0.8408
Epoch 10: loss did not improve from 0.18131
107/400 [=======>......................] - ETA: 5:28 - loss: 0.2359 - dice_coefficient: 0.8416
Epoch 10: loss did not improve from 0.18131
108/400 [=======>......................] - ETA: 5:27 - loss: 0.2355 - dice_coefficient: 0.8416
Epoch 10: loss did not improve from 0.18131
109/400 [=======>......................] - ETA: 5:26 - loss: 0.2352 - dice_coefficient: 0.8421
Epoch 10: loss did not improve from 0.18131
110/400 [=======>......................] - ETA: 5:26 - loss: 0.2363 - dice_coefficient: 0.8409
Epoch 10: loss did not improve from 0.18131
111/400 [=======>......................] - ETA: 5:27 - loss: 0.2355 - dice_coefficient: 0.8415
Epoch 10: loss did not improve from 0.18131
112/400 [=======>......................] - ETA: 5:26 - loss: 0.2366 - dice_coefficient: 0.8405
Epoch 10: loss did not improve from 0.18131
113/400 [=======>......................] - ETA: 5:24 - loss: 0.2355 - dice_coefficient: 0.8412
Epoch 10: loss did not improve from 0.18131
114/400 [=======>......................] - ETA: 5:22 - loss: 0.2353 - dice_coefficient: 0.8418
Epoch 10: loss did not improve from 0.18131
115/400 [=======>......................] - ETA: 5:21 - loss: 0.2349 - dice_coefficient: 0.8418
Epoch 10: loss did not improve from 0.18131
116/400 [=======>......................] - ETA: 5:20 - loss: 0.2347 - dice_coefficient: 0.8421
Epoch 10: loss did not improve from 0.18131
117/400 [=======>......................] - ETA: 5:18 - loss: 0.2335 - dice_coefficient: 0.8430
Epoch 10: loss did not improve from 0.18131
118/400 [=======>......................] - ETA: 5:17 - loss: 0.2327 - dice_coefficient: 0.8437
Epoch 10: loss did not improve from 0.18131
119/400 [=======>......................] - ETA: 5:15 - loss: 0.2318 - dice_coefficient: 0.8443
Epoch 10: loss did not improve from 0.18131
120/400 [========>.....................] - ETA: 5:14 - loss: 0.2329 - dice_coefficient: 0.8432
Epoch 10: loss did not improve from 0.18131
121/400 [========>.....................] - ETA: 5:12 - loss: 0.2334 - dice_coefficient: 0.8426
Epoch 10: loss did not improve from 0.18131
122/400 [========>.....................] - ETA: 5:11 - loss: 0.2330 - dice_coefficient: 0.8431
Epoch 10: loss did not improve from 0.18131
123/400 [========>.....................] - ETA: 5:11 - loss: 0.2346 - dice_coefficient: 0.8417
Epoch 10: loss did not improve from 0.18131
124/400 [========>.....................] - ETA: 5:11 - loss: 0.2337 - dice_coefficient: 0.8421
Epoch 10: loss did not improve from 0.18131
125/400 [========>.....................] - ETA: 5:09 - loss: 0.2371 - dice_coefficient: 0.8399
Epoch 10: loss did not improve from 0.18131
126/400 [========>.....................] - ETA: 5:08 - loss: 0.2365 - dice_coefficient: 0.8403
Epoch 10: loss did not improve from 0.18131
127/400 [========>.....................] - ETA: 5:07 - loss: 0.2369 - dice_coefficient: 0.8398
Epoch 10: loss did not improve from 0.18131
128/400 [========>.....................] - ETA: 5:05 - loss: 0.2374 - dice_coefficient: 0.8401
Epoch 10: loss did not improve from 0.18131
129/400 [========>.....................] - ETA: 5:04 - loss: 0.2372 - dice_coefficient: 0.8406
Epoch 10: loss did not improve from 0.18131
130/400 [========>.....................] - ETA: 5:02 - loss: 0.2364 - dice_coefficient: 0.8411
Epoch 10: loss did not improve from 0.18131
131/400 [========>.....................] - ETA: 5:01 - loss: 0.2404 - dice_coefficient: 0.8383
Epoch 10: loss did not improve from 0.18131
132/400 [========>.....................] - ETA: 5:00 - loss: 0.2398 - dice_coefficient: 0.8388
Epoch 10: loss did not improve from 0.18131
133/400 [========>.....................] - ETA: 4:59 - loss: 0.2401 - dice_coefficient: 0.8386
Epoch 10: loss did not improve from 0.18131
134/400 [=========>....................] - ETA: 4:57 - loss: 0.2421 - dice_coefficient: 0.8369
Epoch 10: loss did not improve from 0.18131
135/400 [=========>....................] - ETA: 4:57 - loss: 0.2410 - dice_coefficient: 0.8376
Epoch 10: loss did not improve from 0.18131
136/400 [=========>....................] - ETA: 4:57 - loss: 0.2413 - dice_coefficient: 0.8372
Epoch 10: loss did not improve from 0.18131
137/400 [=========>....................] - ETA: 4:56 - loss: 0.2401 - dice_coefficient: 0.8380
Epoch 10: loss did not improve from 0.18131
138/400 [=========>....................] - ETA: 4:55 - loss: 0.2416 - dice_coefficient: 0.8367
Epoch 10: loss did not improve from 0.18131
139/400 [=========>....................] - ETA: 4:53 - loss: 0.2408 - dice_coefficient: 0.8372
Epoch 10: loss did not improve from 0.18131
140/400 [=========>....................] - ETA: 4:52 - loss: 0.2400 - dice_coefficient: 0.8377
Epoch 10: loss did not improve from 0.18131
141/400 [=========>....................] - ETA: 4:51 - loss: 0.2400 - dice_coefficient: 0.8376
Epoch 10: loss did not improve from 0.18131
142/400 [=========>....................] - ETA: 4:49 - loss: 0.2421 - dice_coefficient: 0.8359
Epoch 10: loss did not improve from 0.18131
143/400 [=========>....................] - ETA: 4:48 - loss: 0.2412 - dice_coefficient: 0.8366
Epoch 10: loss did not improve from 0.18131
144/400 [=========>....................] - ETA: 4:47 - loss: 0.2402 - dice_coefficient: 0.8373
Epoch 10: loss did not improve from 0.18131
145/400 [=========>....................] - ETA: 4:45 - loss: 0.2393 - dice_coefficient: 0.8378
Epoch 10: loss did not improve from 0.18131
146/400 [=========>....................] - ETA: 4:44 - loss: 0.2385 - dice_coefficient: 0.8383
Epoch 10: loss did not improve from 0.18131
147/400 [==========>...................] - ETA: 4:44 - loss: 0.2390 - dice_coefficient: 0.8380
Epoch 10: loss did not improve from 0.18131
148/400 [==========>...................] - ETA: 4:44 - loss: 0.2381 - dice_coefficient: 0.8386
Epoch 10: loss did not improve from 0.18131
149/400 [==========>...................] - ETA: 4:43 - loss: 0.2371 - dice_coefficient: 0.8392
Epoch 10: loss did not improve from 0.18131
150/400 [==========>...................] - ETA: 4:41 - loss: 0.2360 - dice_coefficient: 0.8399
Epoch 10: loss did not improve from 0.18131
151/400 [==========>...................] - ETA: 4:40 - loss: 0.2352 - dice_coefficient: 0.8405
Epoch 10: loss did not improve from 0.18131
152/400 [==========>...................] - ETA: 4:38 - loss: 0.2352 - dice_coefficient: 0.8406
Epoch 10: loss did not improve from 0.18131
153/400 [==========>...................] - ETA: 4:37 - loss: 0.2400 - dice_coefficient: 0.8378
Epoch 10: loss did not improve from 0.18131
154/400 [==========>...................] - ETA: 4:36 - loss: 0.2400 - dice_coefficient: 0.8375
Epoch 10: loss did not improve from 0.18131
155/400 [==========>...................] - ETA: 4:34 - loss: 0.2397 - dice_coefficient: 0.8377
Epoch 10: loss did not improve from 0.18131
156/400 [==========>...................] - ETA: 4:33 - loss: 0.2399 - dice_coefficient: 0.8375
Epoch 10: loss did not improve from 0.18131
157/400 [==========>...................] - ETA: 4:32 - loss: 0.2393 - dice_coefficient: 0.8380
Epoch 10: loss did not improve from 0.18131
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Epoch 10: loss did not improve from 0.18131
321/400 [=======================>......] - ETA: 1:28 - loss: 0.2736 - dice_coefficient: 0.8459
Epoch 10: loss did not improve from 0.18131
322/400 [=======================>......] - ETA: 1:27 - loss: 0.2735 - dice_coefficient: 0.8460
Epoch 10: loss did not improve from 0.18131
323/400 [=======================>......] - ETA: 1:26 - loss: 0.2733 - dice_coefficient: 0.8460
Epoch 10: loss did not improve from 0.18131
324/400 [=======================>......] - ETA: 1:25 - loss: 0.2729 - dice_coefficient: 0.8460
Epoch 10: loss did not improve from 0.18131
325/400 [=======================>......] - ETA: 1:23 - loss: 0.2723 - dice_coefficient: 0.8463
Epoch 10: loss did not improve from 0.18131
326/400 [=======================>......] - ETA: 1:22 - loss: 0.2718 - dice_coefficient: 0.8466
Epoch 10: loss did not improve from 0.18131
327/400 [=======================>......] - ETA: 1:21 - loss: 0.2718 - dice_coefficient: 0.8465
Epoch 10: loss did not improve from 0.18131
328/400 [=======================>......] - ETA: 1:20 - loss: 0.2713 - dice_coefficient: 0.8467
Epoch 10: loss did not improve from 0.18131
329/400 [=======================>......] - ETA: 1:19 - loss: 0.2708 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
330/400 [=======================>......] - ETA: 1:18 - loss: 0.2706 - dice_coefficient: 0.8471
Epoch 10: loss did not improve from 0.18131
331/400 [=======================>......] - ETA: 1:17 - loss: 0.2719 - dice_coefficient: 0.8462
Epoch 10: loss did not improve from 0.18131
332/400 [=======================>......] - ETA: 1:16 - loss: 0.2716 - dice_coefficient: 0.8463
Epoch 10: loss did not improve from 0.18131
333/400 [=======================>......] - ETA: 1:15 - loss: 0.2713 - dice_coefficient: 0.8465
Epoch 10: loss did not improve from 0.18131
334/400 [========================>.....] - ETA: 1:13 - loss: 0.2711 - dice_coefficient: 0.8467
Epoch 10: loss did not improve from 0.18131
335/400 [========================>.....] - ETA: 1:12 - loss: 0.2707 - dice_coefficient: 0.8468
Epoch 10: loss did not improve from 0.18131
336/400 [========================>.....] - ETA: 1:11 - loss: 0.2706 - dice_coefficient: 0.8468
Epoch 10: loss did not improve from 0.18131
337/400 [========================>.....] - ETA: 1:10 - loss: 0.2701 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
338/400 [========================>.....] - ETA: 1:09 - loss: 0.2698 - dice_coefficient: 0.8471
Epoch 10: loss did not improve from 0.18131
339/400 [========================>.....] - ETA: 1:08 - loss: 0.2694 - dice_coefficient: 0.8473
Epoch 10: loss did not improve from 0.18131
340/400 [========================>.....] - ETA: 1:07 - loss: 0.2689 - dice_coefficient: 0.8475
Epoch 10: loss did not improve from 0.18131
341/400 [========================>.....] - ETA: 1:05 - loss: 0.2695 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
342/400 [========================>.....] - ETA: 1:04 - loss: 0.2695 - dice_coefficient: 0.8467
Epoch 10: loss did not improve from 0.18131
343/400 [========================>.....] - ETA: 1:03 - loss: 0.2690 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
344/400 [========================>.....] - ETA: 1:02 - loss: 0.2687 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
345/400 [========================>.....] - ETA: 1:01 - loss: 0.2684 - dice_coefficient: 0.8471
Epoch 10: loss did not improve from 0.18131
346/400 [========================>.....] - ETA: 1:00 - loss: 0.2683 - dice_coefficient: 0.8471
Epoch 10: loss did not improve from 0.18131
347/400 [=========================>....] - ETA: 59s - loss: 0.2690 - dice_coefficient: 0.8465
Epoch 10: loss did not improve from 0.18131
348/400 [=========================>....] - ETA: 58s - loss: 0.2690 - dice_coefficient: 0.8463
Epoch 10: loss did not improve from 0.18131
349/400 [=========================>....] - ETA: 57s - loss: 0.2688 - dice_coefficient: 0.8463
Epoch 10: loss did not improve from 0.18131
350/400 [=========================>....] - ETA: 55s - loss: 0.2688 - dice_coefficient: 0.8461
Epoch 10: loss did not improve from 0.18131
351/400 [=========================>....] - ETA: 54s - loss: 0.2684 - dice_coefficient: 0.8464
Epoch 10: loss did not improve from 0.18131
352/400 [=========================>....] - ETA: 53s - loss: 0.2680 - dice_coefficient: 0.8466
Epoch 10: loss did not improve from 0.18131
353/400 [=========================>....] - ETA: 52s - loss: 0.2680 - dice_coefficient: 0.8464
Epoch 10: loss did not improve from 0.18131
354/400 [=========================>....] - ETA: 51s - loss: 0.2677 - dice_coefficient: 0.8465
Epoch 10: loss did not improve from 0.18131
355/400 [=========================>....] - ETA: 50s - loss: 0.2672 - dice_coefficient: 0.8468
Epoch 10: loss did not improve from 0.18131
356/400 [=========================>....] - ETA: 49s - loss: 0.2671 - dice_coefficient: 0.8468
Epoch 10: loss did not improve from 0.18131
357/400 [=========================>....] - ETA: 48s - loss: 0.2668 - dice_coefficient: 0.8469
Epoch 10: loss did not improve from 0.18131
358/400 [=========================>....] - ETA: 47s - loss: 0.2666 - dice_coefficient: 0.8469
Epoch 10: loss did not improve from 0.18131
359/400 [=========================>....] - ETA: 45s - loss: 0.2662 - dice_coefficient: 0.8471
Epoch 10: loss did not improve from 0.18131
360/400 [==========================>...] - ETA: 44s - loss: 0.2661 - dice_coefficient: 0.8470
Epoch 10: loss did not improve from 0.18131
361/400 [==========================>...] - ETA: 43s - loss: 0.2658 - dice_coefficient: 0.8472
Epoch 10: loss did not improve from 0.18131
362/400 [==========================>...] - ETA: 42s - loss: 0.2653 - dice_coefficient: 0.8475
Epoch 10: loss did not improve from 0.18131
363/400 [==========================>...] - ETA: 41s - loss: 0.2650 - dice_coefficient: 0.8476
Epoch 10: loss did not improve from 0.18131
364/400 [==========================>...] - ETA: 40s - loss: 0.2647 - dice_coefficient: 0.8478
Epoch 10: loss did not improve from 0.18131
365/400 [==========================>...] - ETA: 39s - loss: 0.2643 - dice_coefficient: 0.8479
Epoch 10: loss did not improve from 0.18131
366/400 [==========================>...] - ETA: 38s - loss: 0.2647 - dice_coefficient: 0.8475
Epoch 10: loss did not improve from 0.18131
367/400 [==========================>...] - ETA: 37s - loss: 0.2644 - dice_coefficient: 0.8476
Epoch 10: loss did not improve from 0.18131
368/400 [==========================>...] - ETA: 35s - loss: 0.2642 - dice_coefficient: 0.8476
Epoch 10: loss did not improve from 0.18131
369/400 [==========================>...] - ETA: 34s - loss: 0.2638 - dice_coefficient: 0.8478
Epoch 10: loss did not improve from 0.18131
370/400 [==========================>...] - ETA: 33s - loss: 0.2641 - dice_coefficient: 0.8474
Epoch 10: loss did not improve from 0.18131
371/400 [==========================>...] - ETA: 32s - loss: 0.2636 - dice_coefficient: 0.8477
Epoch 10: loss did not improve from 0.18131
372/400 [==========================>...] - ETA: 31s - loss: 0.2635 - dice_coefficient: 0.8477
Epoch 10: loss did not improve from 0.18131
373/400 [==========================>...] - ETA: 30s - loss: 0.2630 - dice_coefficient: 0.8480
Epoch 10: loss did not improve from 0.18131
374/400 [===========================>..] - ETA: 29s - loss: 0.2629 - dice_coefficient: 0.8479
Epoch 10: loss did not improve from 0.18131
375/400 [===========================>..] - ETA: 27s - loss: 0.2625 - dice_coefficient: 0.8481
Epoch 10: loss did not improve from 0.18131
376/400 [===========================>..] - ETA: 26s - loss: 0.2622 - dice_coefficient: 0.8483
Epoch 10: loss did not improve from 0.18131
377/400 [===========================>..] - ETA: 25s - loss: 0.2618 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
378/400 [===========================>..] - ETA: 24s - loss: 0.2617 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
379/400 [===========================>..] - ETA: 23s - loss: 0.2616 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
380/400 [===========================>..] - ETA: 22s - loss: 0.2613 - dice_coefficient: 0.8486
Epoch 10: loss did not improve from 0.18131
381/400 [===========================>..] - ETA: 21s - loss: 0.2610 - dice_coefficient: 0.8487
Epoch 10: loss did not improve from 0.18131
382/400 [===========================>..] - ETA: 20s - loss: 0.2609 - dice_coefficient: 0.8487
Epoch 10: loss did not improve from 0.18131
383/400 [===========================>..] - ETA: 19s - loss: 0.2614 - dice_coefficient: 0.8482
Epoch 10: loss did not improve from 0.18131
384/400 [===========================>..] - ETA: 17s - loss: 0.2610 - dice_coefficient: 0.8484
Epoch 10: loss did not improve from 0.18131
385/400 [===========================>..] - ETA: 16s - loss: 0.2607 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
386/400 [===========================>..] - ETA: 15s - loss: 0.2609 - dice_coefficient: 0.8482
Epoch 10: loss did not improve from 0.18131
387/400 [============================>.] - ETA: 14s - loss: 0.2605 - dice_coefficient: 0.8484
Epoch 10: loss did not improve from 0.18131
388/400 [============================>.] - ETA: 13s - loss: 0.2603 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
389/400 [============================>.] - ETA: 12s - loss: 0.2599 - dice_coefficient: 0.8487
Epoch 10: loss did not improve from 0.18131
390/400 [============================>.] - ETA: 11s - loss: 0.2602 - dice_coefficient: 0.8484
Epoch 10: loss did not improve from 0.18131
391/400 [============================>.] - ETA: 10s - loss: 0.2598 - dice_coefficient: 0.8486
Epoch 10: loss did not improve from 0.18131
392/400 [============================>.] - ETA: 8s - loss: 0.2597 - dice_coefficient: 0.8485
Epoch 10: loss did not improve from 0.18131
393/400 [============================>.] - ETA: 7s - loss: 0.2595 - dice_coefficient: 0.8486
Epoch 10: loss did not improve from 0.18131
394/400 [============================>.] - ETA: 6s - loss: 0.2595 - dice_coefficient: 0.8484
Epoch 10: loss did not improve from 0.18131
395/400 [============================>.] - ETA: 5s - loss: 0.2593 - dice_coefficient: 0.8484
Epoch 10: loss did not improve from 0.18131
396/400 [============================>.] - ETA: 4s - loss: 0.2589 - dice_coefficient: 0.8487
Epoch 10: loss did not improve from 0.18131
397/400 [============================>.] - ETA: 3s - loss: 0.2585 - dice_coefficient: 0.8489
Epoch 10: loss did not improve from 0.18131
398/400 [============================>.] - ETA: 2s - loss: 0.2583 - dice_coefficient: 0.8491
Epoch 10: loss did not improve from 0.18131
399/400 [============================>.] - ETA: 1s - loss: 0.2580 - dice_coefficient: 0.8492
Epoch 10: loss did not improve from 0.18131
400/400 [==============================] - 451s 1s/step - loss: 0.2577 - dice_coefficient: 0.8493 - val_loss: 0.7984 - val_dice_coefficient: 0.5841 - lr: 1.0000e-04
<keras.callbacks.History at 0x7fb2b8a35480>
2D.Evaluate and share insights on performance of the model.
model.evaluate(X_test, y_test, verbose = 1)
1/1 [==============================] - 4s 4s/step - loss: 0.5927 - dice_coefficient: 0.6809
[0.592660129070282, 0.6809486150741577]
Observation:
Model uses a pretrained MobileNet with UNET layers to train, fit and predict masks around the face in a given image. With binary cross entropy as loss, adam optimizer and dice coefficient as metrics, loss of 0.2577 and dice coeff of 0.8493 was achieved. Testing it on the test images, the model does a very good job in predicting the masks. So will go with this model for further evaluation.
# Show the test image
plt.imshow(X_test[3]);
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
# Predicting the mask for the test image
pred_mask = cv2.resize(1.0 * (model.predict(x=np.array([X_test[3]]))[0] > 0.5), (IMAGE_WIDTH, IMAGE_HEIGHT))
plt.imshow(X_test[3])
plt.imshow(pred_mask, alpha=0.3)
1/1 [==============================] - 1s 727ms/step
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<matplotlib.image.AxesImage at 0x7fb2b96d8940>
plt.imshow(pred_mask, alpha = 0.3)
<matplotlib.image.AxesImage at 0x7fb2af07e050>
• DOMAIN: Entertainment
• CONTEXT: Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene.The data labelling is already done. Since there higher time complexity is involved in the
• DATA DESCRIPTION: The dataset comprises of face images.
• PROJECT OBJECTIVE: To create an image dataset to be used by AI team build an image classifier data. Profile images of people are given.
1.Read/import images from folder ‘training_images’.
# Setting the current working directory
import os;
os.chdir('/content/drive/MyDrive/Colab Notebooks/')
# Extract content from zipfile
from zipfile import ZipFile
with ZipFile('training_images-20211126T092819Z-001.zip', 'r') as zip:
zip.extractall()
2.Write a loop which will iterate through all the images in the ‘training_images’ folder and detect the faces present on all the images.
3.From the same loop above, extract metadata of the faces and write into a DataFrame.
# create an Empty DataFrame object With column names
df = pd.DataFrame(columns = ['ImageName','FacePresent', 'x', 'y', 'w', 'h'])
df
| ImageName | FacePresent | x | y | w | h |
|---|
image_list = '/content/drive/MyDrive/Colab Notebooks/training_images'
face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + '/haarcascade_frontalface_default.xml')
for i in os.listdir(os.path.join(image_list)):
if i != '.DS_Store':
# Load the image
img = cv2.imread(os.path.join(image_list, i))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
# No faces found
df = df.append({'ImageName' : i, 'FacePresent' : 0}, ignore_index = True)
else:
# Classifier returns 4 values — x, y, w and h of the detected feature of the face.
for (x,y,w,h) in faces:
df = df.append({'ImageName' : i, 'FacePresent' : 1, 'x': x, 'y': y, 'w' : w, 'h': h},
ignore_index = True)
cv2.destroyAllWindows()
df.sort_values(by=['ImageName'])
| ImageName | FacePresent | x | y | w | h | |
|---|---|---|---|---|---|---|
| 993 | real_00001.jpg | 1 | 134 | 125 | 430 | 430 |
| 972 | real_00002.jpg | 1 | 79 | 143 | 430 | 430 |
| 982 | real_00003.jpg | 1 | 34 | 116 | 369 | 369 |
| 980 | real_00004.jpg | 1 | 122 | 157 | 380 | 380 |
| 987 | real_00005.jpg | 0 | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... |
| 1038 | real_01077.jpg | 1 | 86 | 162 | 405 | 405 |
| 45 | real_01078.jpg | 1 | 65 | 70 | 475 | 475 |
| 1053 | real_01079.jpg | 1 | 65 | 63 | 478 | 478 |
| 1045 | real_01080.jpg | 1 | 49 | 84 | 452 | 452 |
| 27 | real_01081.jpg | 0 | NaN | NaN | NaN | NaN |
1092 rows × 6 columns
from matplotlib.patches import Rectangle
# Display random image and its bounding box for face detection to verify
df1 = df.iloc[993]
img = cv2.imread(os.path.join(image_list, df1['ImageName']))
plt.imshow(img)
plt.gca().add_patch(Rectangle((df1['x'],df1['y']),df1['w'],df1['h'],linewidth=1,edgecolor='r',facecolor='none'))
<matplotlib.patches.Rectangle at 0x7fb2aef051b0>
4.Save the output Dataframe in .csv format.
# saving the dataframe
df.to_csv('file1.csv')
• DOMAIN: Face Recognition
• CONTEXT: Company X intends to build a face identification model to recognise human faces.
• DATA DESCRIPTION: The dataset comprises of images and its mask where there is a human face.
• PROJECT OBJECTIVE: Face Aligned Face Dataset from Pinterest. This dataset contains 10,770 images for 100 people. All images are taken from 'Pinterest' and aligned using dlib library.
1.Unzip, read and Load data(‘PINS.zip’) into session.
# Setting the current working directory
import os;
os.chdir('/content/drive/MyDrive/Colab Notebooks/')
# Extract content from zipfile
from zipfile import ZipFile
with ZipFile('PINS.zip', 'r') as zip:
zip.extractall()
2.Write function to create metadata of the image.
import numpy as np
import os
class IdentityMetadata():
def __init__(self, base, name, file):
# dataset base directory
self.base = base
# identity name
self.name = name
# image file name
self.file = file
def __repr__(self):
return self.image_path()
def image_path(self):
return os.path.join(self.base, self.name, self.file)
3.Write a loop to iterate through each and every image and create metadata for all the images.
def load_metadata(path):
metadata = []
for i in os.listdir(path):
for f in os.listdir(os.path.join(path, i)):
# Check file extension. Allow only jpg/jpeg' files.
ext = os.path.splitext(f)[1]
if ext == '.jpg' or ext == '.jpeg':
metadata.append(IdentityMetadata(path, i, f))
return np.array(metadata)
# Call load_metadata to read the images and assign metadata to a variable
metadata = load_metadata('PINS')
labels = np.array([meta.name for meta in metadata])
metadata[0]
PINS/pins_Aaron Paul/Aaron Paul0_262.jpg
labels
array(['pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul', ...,
'pins_zendaya', 'pins_zendaya', 'pins_zendaya'], dtype='<U29')
# Write a function to read image using opencv and convert it from BGR to RGB
def load_image(path):
img = cv2.imread(path, 1)
# Convert the image order from BGR to RGB
return img[...,::-1]
load_image(metadata[0].image_path())
array([[[233, 188, 165],
[231, 186, 163],
[220, 178, 156],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
[[232, 187, 164],
[233, 188, 165],
[220, 178, 156],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
[[230, 186, 161],
[228, 184, 159],
[206, 164, 140],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
...,
[[198, 118, 91],
[197, 120, 94],
[195, 119, 95],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]],
[[200, 120, 93],
[200, 120, 93],
[198, 121, 95],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]],
[[201, 121, 94],
[201, 121, 94],
[199, 122, 96],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]]], dtype=uint8)
4.Generate Embeddings vectors on the each face in the dataset.
from keras.models import Sequential
from keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation
def vgg_face():
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
return model
# Load the model using "vgg_face_weights.h5"
model = vgg_face()
model.load_weights('vgg_face_weights.h5')
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d (ZeroPadding (None, 226, 226, 3) 0
2D)
conv2d (Conv2D) (None, 224, 224, 64) 1792
zero_padding2d_1 (ZeroPaddi (None, 226, 226, 64) 0
ng2D)
conv2d_1 (Conv2D) (None, 224, 224, 64) 36928
max_pooling2d (MaxPooling2D (None, 112, 112, 64) 0
)
zero_padding2d_2 (ZeroPaddi (None, 114, 114, 64) 0
ng2D)
conv2d_2 (Conv2D) (None, 112, 112, 128) 73856
zero_padding2d_3 (ZeroPaddi (None, 114, 114, 128) 0
ng2D)
conv2d_3 (Conv2D) (None, 112, 112, 128) 147584
max_pooling2d_1 (MaxPooling (None, 56, 56, 128) 0
2D)
zero_padding2d_4 (ZeroPaddi (None, 58, 58, 128) 0
ng2D)
conv2d_4 (Conv2D) (None, 56, 56, 256) 295168
zero_padding2d_5 (ZeroPaddi (None, 58, 58, 256) 0
ng2D)
conv2d_5 (Conv2D) (None, 56, 56, 256) 590080
zero_padding2d_6 (ZeroPaddi (None, 58, 58, 256) 0
ng2D)
conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
max_pooling2d_2 (MaxPooling (None, 28, 28, 256) 0
2D)
zero_padding2d_7 (ZeroPaddi (None, 30, 30, 256) 0
ng2D)
conv2d_7 (Conv2D) (None, 28, 28, 512) 1180160
zero_padding2d_8 (ZeroPaddi (None, 30, 30, 512) 0
ng2D)
conv2d_8 (Conv2D) (None, 28, 28, 512) 2359808
zero_padding2d_9 (ZeroPaddi (None, 30, 30, 512) 0
ng2D)
conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
max_pooling2d_3 (MaxPooling (None, 14, 14, 512) 0
2D)
zero_padding2d_10 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_10 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_11 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_12 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
max_pooling2d_4 (MaxPooling (None, 7, 7, 512) 0
2D)
conv2d_13 (Conv2D) (None, 1, 1, 4096) 102764544
dropout (Dropout) (None, 1, 1, 4096) 0
conv2d_14 (Conv2D) (None, 1, 1, 4096) 16781312
dropout_1 (Dropout) (None, 1, 1, 4096) 0
conv2d_15 (Conv2D) (None, 1, 1, 2622) 10742334
flatten (Flatten) (None, 2622) 0
activation (Activation) (None, 2622) 0
=================================================================
Total params: 145,002,878
Trainable params: 145,002,878
Non-trainable params: 0
_________________________________________________________________
from keras.models import Model
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
# Get embedding vector for first image in the metadata using the pre-trained model
img_path = metadata[0].image_path()
img = load_image(img_path)
# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)
img = cv2.resize(img, dsize = (224,224))
print(img.shape)
# Obtain embedding vector for an image
# Get the embedding vector for the above image using vgg_face_descriptor model and print the shape
embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]
print(embedding_vector.shape)
(224, 224, 3) 1/1 [==============================] - 2s 2s/step (2622,)
Write code to iterate through metadata and create embeddings for each image using vgg_face_descriptor.predict() and store in a list with name embeddings
If there is any error in reading any image in the dataset, fill the emebdding vector of that image with 2622-zeroes as the final embedding from the model is of length 2622.
embeddings = np.zeros((metadata.shape[0], 2622))
for i, m in enumerate(metadata):
try:
img = load_image(str(m))
# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)
img = cv2.resize(img, dsize = (224,224))
# Obtain embedding vector for an image
# Get the embedding vector for the above image using vgg_face_descriptor model and print the shape
embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]
except :
embeddings[i] = np.zeros(2622)
Streaming output truncated to the last 5000 lines.
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embeddings
array([[ 0.03170305, -0.0150513 , -0.01243403, ..., 0.00043139,
0.00219082, -0.00908097],
[ 0.03497702, -0.0010506 , -0.01248935, ..., -0.01053092,
0.00179321, 0.02439155],
[ 0.02801892, -0.00112631, -0.01265748, ..., -0.00904752,
-0.00578403, 0.02125833],
...,
[ 0.00965871, 0.0037216 , -0.01345863, ..., -0.01433404,
0.01899945, 0.03899154],
[ 0.0023093 , 0.00094764, -0.00544814, ..., -0.01600496,
0.01053976, 0.03683251],
[ 0.01772303, 0.01064051, 0.01995193, ..., -0.02618429,
0.03288927, 0.04882647]])
5.Build distance metrics for identifying the distance between two similar and dissimilar images.
Consider distance metric as "Squared L2 distance"
Squared l2 distance between 2 points (x1, y1) and (x2, y2) = (x1-x2)^2 + (y1-y2)^2
def distance(emb1, emb2):
return np.sum(np.square(emb1 - emb2))
Plot images and get distance between the pairs given below:
2, 3 and 2, 180
30, 31 and 30, 100
70, 72 and 70, 115
import matplotlib.pyplot as plt
def show_pair(idx1, idx2):
plt.figure(figsize=(8,3))
plt.suptitle(f'Distance = {distance(embeddings[idx1], embeddings[idx2]):.2f}')
plt.subplot(121)
plt.imshow(load_image(metadata[idx1].image_path()))
plt.subplot(122)
plt.imshow(load_image(metadata[idx2].image_path()));
show_pair(2, 3)
show_pair(2, 180)
show_pair(30, 31)
show_pair(30, 100)
show_pair(70, 72)
show_pair(70, 115)
6.Use PCA for dimensionality reduction.
train_idx = np.arange(metadata.shape[0]) % 9 != 0
test_idx = np.arange(metadata.shape[0]) % 9 == 0
# one half as train examples of 10 identities
X_train = embeddings[train_idx]
# another half as test examples of 10 identities
X_test = embeddings[test_idx]
targets = np.array([m.name for m in metadata])
y_train = targets[train_idx]
y_test = targets[test_idx]
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((9573, 2622), (1197, 2622), (9573,), (1197,))
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_test = encoder.transform(y_test)
# Standarize features
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
Reduce feature dimensions using Principal Component Analysis
Set the parameter n_components=128
from sklearn.decomposition import PCA
pca = PCA(n_components = 128)
pca.fit(X_train_scaled)
PCA(n_components=128)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA(n_components=128)
np.cumsum(pca.explained_variance_ratio_)
array([0.1357609 , 0.18945183, 0.22921166, 0.26610786, 0.30012142,
0.33163744, 0.35747561, 0.38100236, 0.40202276, 0.42128208,
0.43946508, 0.45647645, 0.47300773, 0.48866813, 0.50285268,
0.51653456, 0.52960487, 0.54180207, 0.55332455, 0.56466581,
0.57559892, 0.58620426, 0.59586128, 0.60526518, 0.61436752,
0.6231415 , 0.63151444, 0.63924616, 0.64692376, 0.65429381,
0.66133862, 0.66789378, 0.67422692, 0.68045516, 0.68611114,
0.69148374, 0.69679652, 0.7019387 , 0.70696206, 0.71181333,
0.71626359, 0.72056511, 0.72455557, 0.72850285, 0.73222005,
0.73578208, 0.73923555, 0.7425718 , 0.7458432 , 0.7490274 ,
0.75216335, 0.75523966, 0.75814061, 0.76095683, 0.76373124,
0.76646075, 0.76915928, 0.77180056, 0.77431922, 0.77682643,
0.77920854, 0.78153019, 0.78377779, 0.78594515, 0.78808441,
0.7901309 , 0.79214711, 0.79408514, 0.79597775, 0.79782199,
0.79965004, 0.80142124, 0.8031769 , 0.8049004 , 0.80658028,
0.80822774, 0.80986027, 0.81145613, 0.81304361, 0.81460267,
0.81613853, 0.81762971, 0.81907937, 0.82050015, 0.82191595,
0.82330576, 0.82467522, 0.82601321, 0.82732315, 0.82861465,
0.82989248, 0.83112922, 0.83236067, 0.83358779, 0.83479967,
0.83600898, 0.83718856, 0.83835405, 0.83951428, 0.84062875,
0.84172996, 0.84281686, 0.84390153, 0.84496625, 0.84602838,
0.84706261, 0.84809493, 0.84910605, 0.85010875, 0.85108329,
0.85204058, 0.85299188, 0.85393103, 0.85485982, 0.85577717,
0.8566873 , 0.85759375, 0.85847849, 0.85935959, 0.86022131,
0.86107731, 0.86192547, 0.86277238, 0.86359765, 0.86440254,
0.86520348, 0.86599006, 0.86676203])
# generating the covariance matrix and the eigen values for the PCA analysis
cov_matrix = np.cov(X_train_scaled.T) # the relevanat covariance matrix
#generating the eigen values and the eigen vectors
e_vals, e_vecs = np.linalg.eig(cov_matrix)
# the "cumulative variance explained" analysis
tot = sum(e_vals)
var_exp = [( i /tot ) * 100 for i in sorted(e_vals, reverse=True)]
cum_var_exp = np.cumsum(var_exp)
# Plotting the Cumulative Summation of the Explained Variance
plt.rcParams['figure.figsize'] = (10, 10)
plot = sns.pointplot(x = list(range(1,129)), y = np.cumsum(pca.explained_variance_ratio_), color='black')
plot.set_xlabel('Number of Components')
plot.set_ylabel('Variance (%)')
plot.set_title('Explained Variance');
X_train_pca = pca.transform(X_train_scaled)
X_test_pca = pca.transform(X_test_scaled)
print(X_train_pca.shape)
print(X_test_pca.shape)
(9573, 128) (1197, 128)
7.Build an SVM classifier in order to map each image to its right person.
Train the SVM classifier and print the score
from sklearn.svm import SVC
params= {'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']}
svc = GridSearchCV(SVC(random_state = 0), params, cv = 3, scoring = 'f1_macro', verbose = 50, n_jobs = -1)
svc.fit(X_train_pca, y_train)
print('Best estimator found by grid search:')
print(svc.best_estimator_)
Fitting 3 folds for each of 25 candidates, totalling 75 fits Best estimator found by grid search: SVC(C=10, gamma=0.0001, random_state=0)
Best Params from Grid Search:
SVC(C=10, class_weight='balanced', gamma=0.0001, random_state=0)
We will use the same to create the model.
svc = SVC(C = 10, gamma = 0.0001, kernel = 'rbf', class_weight = 'balanced', random_state = 0)
svc.fit(X_train_pca, y_train)
SVC(C=10, class_weight='balanced', gamma=0.0001, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(C=10, class_weight='balanced', gamma=0.0001, random_state=0)
y_train_pred = svc.predict(X_train_pca)
print('Accuracy Score for Training set: {}'.format(accuracy_score(y_train, y_train_pred).round(3)))
Accuracy Score for Training set: 0.998
from sklearn.metrics import accuracy_score
# Predict
y_pred = svc.predict(X_test_pca)
print('Accuracy Score for Test set: {}'.format(accuracy_score(y_test, y_pred).round(3)))
Accuracy Score for Test set: 0.957
Accuracy score for test set is around 96% whereas training data is 99.8% indicating overfit model.
labels
array(['pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul', ...,
'pins_zendaya', 'pins_zendaya', 'pins_zendaya'], dtype='<U29')
All the names start with 'pins'. Lets remove the pins part and display the names.
Names = [name.split('_')[1].title().strip() for name in labels]
# Classification Report
print('Classification Report: \n{}'.format(classification_report(y_test, y_pred, target_names = np.unique(Names))))
Classification Report:
precision recall f1-score support
Aaron Paul 0.91 1.00 0.95 10
Alexandra Daddario 1.00 1.00 1.00 10
Alvaro Morte 0.93 1.00 0.97 14
Alycia Debnam Carey Face 1.00 1.00 1.00 12
Amanda Crew 0.86 0.86 0.86 7
Amaury Nolasco 1.00 0.89 0.94 9
Amber Heard Face 0.78 1.00 0.88 7
Anna Gunn 0.93 1.00 0.97 14
Anne Hathaway 0.93 1.00 0.97 14
Barbara Palvin Face 0.89 0.89 0.89 9
Bellamy Blake Face 0.93 0.93 0.93 14
Benedict Cumberbatch 1.00 1.00 1.00 12
Betsy Brandt 1.00 1.00 1.00 10
Bill Gates 1.00 1.00 1.00 10
Brenton Thwaites 1.00 1.00 1.00 15
Brie Larson 0.94 1.00 0.97 15
Brit Marling 0.92 1.00 0.96 12
Bryan Cranston 1.00 1.00 1.00 14
Caity Lotz 1.00 0.92 0.96 12
Cameron Monaghan 1.00 1.00 1.00 14
Chadwick Boseman Face 0.85 1.00 0.92 17
Chance Perdomo 0.90 1.00 0.95 9
Chris Evans 1.00 0.93 0.96 14
Chris Pratt 1.00 0.93 0.96 14
Cobie Smulders 0.88 1.00 0.94 15
Danielle Panabaker 1.00 0.93 0.96 14
Dave Franco 1.00 0.92 0.96 13
David Mazouz 1.00 0.91 0.95 11
Dominic Purcell 0.92 1.00 0.96 12
Drake 1.00 0.71 0.83 7
Dua Lipa Face 1.00 1.00 1.00 11
Dwayne Johnson 1.00 1.00 1.00 12
Eliza Taylor 1.00 1.00 1.00 13
Elizabeth Olsen Face 1.00 1.00 1.00 10
Elon Musk 0.89 1.00 0.94 17
Emilia Clarke 1.00 0.92 0.96 13
Emily Bett Rickards 1.00 0.80 0.89 5
Emma Stone 0.93 1.00 0.97 14
Emma Watson Face 1.00 1.00 1.00 8
Gal Gadot Face 1.00 0.79 0.88 14
Grant Gustin Face 1.00 1.00 1.00 11
Gwyneth Paltrow 1.00 1.00 1.00 13
Henry Cavil 1.00 1.00 1.00 8
Jason Isaacs 1.00 0.93 0.96 14
Jason Momoa 0.73 0.89 0.80 9
Jeff Bezos 0.91 1.00 0.95 10
Jeremy Renner 1.00 1.00 1.00 8
Jesse Eisenberg 1.00 1.00 1.00 9
Jim Parsons 1.00 1.00 1.00 10
Jon Bernthal 0.92 1.00 0.96 11
Josh Radnor 1.00 1.00 1.00 11
Kiernan Shipka 1.00 0.93 0.96 14
Kit Harington 1.00 1.00 1.00 16
Kristen Stewart Face 1.00 1.00 1.00 12
Krysten Ritter 1.00 1.00 1.00 13
Kumail Nanjiani 0.85 1.00 0.92 11
Lindsey Morgan Face 1.00 1.00 1.00 10
Maisie Williams 1.00 0.89 0.94 9
Margot Robbie Face 0.92 1.00 0.96 12
Maria Pedraza 0.82 1.00 0.90 9
Mark Ruffalo 0.86 1.00 0.92 12
Mark Zuckerberg 1.00 1.00 1.00 17
Martin Starr 1.00 0.93 0.97 15
Melissa Benoit 1.00 0.94 0.97 18
Miguel Herran 1.00 1.00 1.00 16
Mike Colter 1.00 1.00 1.00 17
Millie Bobby Brown 1.00 1.00 1.00 17
Morena Baccarin 1.00 0.94 0.97 16
Morgan Freeman 0.88 0.70 0.78 10
Natalie Portman 1.00 1.00 1.00 10
Neil Patrick Harris 1.00 0.86 0.92 14
Paul Rudd 1.00 1.00 1.00 13
Pedro Alonso 1.00 0.83 0.91 12
Peter Dinklage 1.00 1.00 1.00 4
Rami Melek 1.00 1.00 1.00 15
Rihanna 0.75 0.75 0.75 12
Rj Mitte 1.00 1.00 1.00 20
Robert Downey Jr Face 1.00 1.00 1.00 9
Robert Knepper 1.00 0.78 0.88 18
Robin Taylor 0.89 0.89 0.89 18
Ryan Reynolds 1.00 1.00 1.00 13
Sarah Wayne Callies 0.93 1.00 0.97 14
Scarlett Johansson 1.00 0.90 0.95 10
Sean Pertwee 1.00 1.00 1.00 18
Sebastian Stan 0.80 0.92 0.86 13
Selena Gomez 0.88 0.88 0.88 8
Shakira 1.00 0.93 0.97 15
Sophie Turner 0.75 0.75 0.75 8
Stephen Amell 1.00 1.00 1.00 7
Sundar Pichai 0.90 1.00 0.95 9
Tati Gabrielle 1.00 0.89 0.94 9
Taylor Swift 1.00 1.00 1.00 13
Thomas Middleditch 1.00 1.00 1.00 12
Tom Cavanagh 0.80 0.89 0.84 9
Tom Holland Face 1.00 1.00 1.00 10
Ursula Corbero 1.00 1.00 1.00 6
Wentworth Miller 1.00 0.86 0.92 7
Willa Holland 0.85 1.00 0.92 11
William Fichtner 1.00 0.92 0.96 13
Zendaya 1.00 0.92 0.96 12
accuracy 0.96 1197
macro avg 0.96 0.95 0.95 1197
weighted avg 0.96 0.96 0.96 1197
8. Import and display the the test images.
test_image_1 = cv2.imread('Benedict+Cumberbatch9.jpg')
plt.rcParams['figure.figsize'] = (5, 5)
plt.imshow(test_image_1);
test_image_2 = cv2.imread('Dwayne+Johnson4.jpg')
plt.rcParams['figure.figsize'] = (5, 5)
plt.imshow(test_image_2);
9.Use the trained SVM model to predict the face on both test images.
def predict_person( test_image):
# Normalizing pixel values
test_image = (test_image/255.).astype(np.float32)
# Resize
test_image = cv2.resize(test_image, (224, 224))
# Obtain embedding vector for test image
embedding = vgg_face_descriptor.predict(np.expand_dims(test_image, axis = 0))[0]
# Predict
pred = svc.predict(pca.transform(scaler.transform(embedding.reshape(1, -1))))
# Transform back
pred_name = encoder.inverse_transform(pred)[0].split('_')[-1].title().strip()
plt.rcParams['figure.figsize'] = (5, 5)
plt.axis('off')
plt.imshow(test_image);
plt.title(pred_name)
predict_person(test_image_1)
1/1 [==============================] - 0s 39ms/step
predict_person(test_image_2)
1/1 [==============================] - 0s 24ms/step
Observations:
The Model uses VGG with pre-trained weights.
Normalization, label encoder were performed on data.
PCA was used to reduce the dimensions.
It was advised to use SVC model. Hence using the grid search, best params were retrieved and accuracy of 96% was achieved on the test data.
Given test images were predicted and the model was able to detect the face correctly.